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://scar-tutorials.readthedocs.io/en/main/
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cite-seq crispr-screen denoising-algorithm generative-model machine-learning probabilistic-graphical-models pytorch single-cell-rna-seq variational-autoencoder

scAR install with bioconda code style: black Documentation Status semantic-release: angular test Stars Downloads

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

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}
}