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Scar

scAR (single-cell Ambient Remover) is a deep learning model for removal of the ambient signals in droplet-based single cell omics

From Novartis·Updated April 24, 2026·View on GitHub·

**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. The project is written primarily in Python, first published in 2022. Key topics include: cite-seq, crispr-screen, denoising-algorithm, generative-model, machine-learning.

Latest release: v0.7.0
August 14, 2024View Changelog →
<p align="left"> <img src="docs/_static/scAR_logo_white.png" width="250" title="scAR"> </p>

scAR
install with bioconda
code style: black
Documentation Status
semantic-release: angular
test
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scAR (<u>s</u>ingle-<u>c</u>ell <u>A</u>mbient <u>R</u>emover) 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

Dependencies

PyTorch 1.8
Python 3.8.6
torchvision 0.9.0
tqdm 4.62.3
scikit-learn 1.0.1

Resources

License

This project is licensed under the terms of License.
Copyright 2022 Novartis International AG.

Reference

If you use scAR in your research, please consider citing our manuscript,

@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}
}

Contributors

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This article is auto-generated from Novartis/scar via the GitHub API.Last fetched: 6/19/2026