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https://github.com/Novartis/scar
scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics
https://github.com/Novartis/scar
cite-seq crispr-screen denoising-algorithm generative-model machine-learning probabilistic-graphical-models pytorch single-cell-rna-seq variational-autoencoder
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scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics
- Host: GitHub
- URL: https://github.com/Novartis/scar
- Owner: Novartis
- Created: 2022-03-15T20:35:43.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-28T21:20:32.000Z (4 months ago)
- Last Synced: 2024-05-28T21:33:01.358Z (4 months ago)
- Topics: cite-seq, crispr-screen, denoising-algorithm, generative-model, machine-learning, probabilistic-graphical-models, pytorch, single-cell-rna-seq, variational-autoencoder
- Language: Python
- Homepage: https://scar-tutorials.readthedocs.io/en/main/
- Size: 60 MB
- Stars: 44
- Watchers: 8
- Forks: 4
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Contributing: .github/CONTRIBUTING.md
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README
[](https://anaconda.org/bioconda/scar)
[](http://bioconda.github.io/recipes/scar/README.html)
[](https://github.com/psf/black)
[](https://scar-tutorials.readthedocs.io/en/latest/?badge=latest)
[](https://github.com/semantic-release/semantic-release)
[](https://github.com/Novartis/scAR/actions/workflows/python-conda-build.yaml)
[](https://github.com/Novartis/scAR)
[](https://anaconda.org/bioconda/scar/files)
**scAR** (single-cell Ambient Remover) is a tool designed for denoising ambient signals in droplet-based single-cell omics data. It can be employed for a wide range of applications, such as, **sgRNA assignment** in scCRISPRseq, **identity barcode assignment** in cell indexing, **protein denoising** in CITE-seq, **mRNA denoising** in scRNAseq, and **ATAC signal denoising** in scATACseq, among others.
# Table of Contents
- [Installation](#Installation)
- [Dependencies](#Dependencies)
- [Resources](#Resources)
- [License](#License)
- [Reference](#Reference)
## [Installation](https://scar-tutorials.readthedocs.io/en/latest/Installation.html)
## Dependencies
[](https://pytorch.org/)
[](https://www.python.org/)
[](https://pytorch.org/vision/stable/index.html)
[](https://github.com/tqdm/tqdm)
[](https://scikit-learn.org/)
## Resources
- Installation, Usages and Tutorials can be found in the [documentation](https://scar-tutorials.readthedocs.io/en/latest/).
- If you'd like to contribute, please read [contributing guidelines](https://github.com/Novartis/scAR/blob/main/.github/CONTRIBUTING.md).
- Please use the [issues](https://github.com/Novartis/scAR/issues) to submit bug reports.
## License
This project is licensed under the terms of [License](docs/License.rst).
Copyright 2022 Novartis International AG.
## Reference
If you use scAR in your research, please consider citing our [manuscript](https://doi.org/10.1101/2022.01.14.476312),
```
@article {Sheng2022.01.14.476312,
author = {Sheng, Caibin and Lopes, Rui and Li, Gang and Schuierer, Sven and Waldt, Annick and Cuttat, Rachel and Dimitrieva, Slavica and Kauffmann, Audrey and Durand, Eric and Galli, Giorgio G and Roma, Guglielmo and de Weck, Antoine},
title = {Probabilistic modeling of ambient noise in single-cell omics data},
elocation-id = {2022.01.14.476312},
year = {2022},
doi = {10.1101/2022.01.14.476312},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2022/01/14/2022.01.14.476312},
eprint = {https://www.biorxiv.org/content/early/2022/01/14/2022.01.14.476312.full.pdf},
journal = {bioRxiv}
}
```