single-cell omics
machine learning
variational autoencoders

scAR (single cell Ambient Remover) is a package for denoising multiple single cell omics data. It can be used for multiple tasks, such as, sgRNA assignment for scCRISPRseq, identity barcode assignment for cell indexing, protein denoising for CITE-seq, mRNA denoising for scRNAseq, and etc… It is built using probabilistic deep learning, illustrated as follows:

Table of Contents


Clone this repository,

$ git clone

To install the dependencies, create a conda environment:

$ conda env create -f scAR.yml

Locate to scAR directory:

$ cd scAR

Pip install scAR:

$ pip install .


There are two ways to run scAR.

  1. Use scAR API if you are Python users

>>> scarObj = scAR.model(adata.X.to_df(), empty_profile)
>>> scarObj.train()
>>> scarObj.inference()
>>> adata.layers["X_scAR_denoised"] = scarObj.native_counts
>>> adata.obsm["X_scAR_assignment"] = scarObj.feature_assignment  # feature assignment, e.g., sgRNAs, tags, and etc.. Only available in 'cropseq' mode

See the tutorials

  1. Run scAR from the command line
$ scar raw_count_matrix.pickle -t technology -e empty_profile.pickle -o output

raw_count_matrix.pickle, a pickle-formatted raw count matrix (MxN) with cells in rows and features in columns
empty_profile.pickle, a pickle-formatted feature frequencies (Nx1) in empty droplets
technology, a string, either ‘scRNAseq’ or ‘CROPseq’ or ‘CITEseq’

Use scar --help command to see other optional arguments and parameters.

The output folder contains four (or five) files:

├── denoised_counts.pickle		# denoised count matrix
├── expected_noise_ratio.pickle	# estimated noise ratio
├── BayesFactor.pickle			# bayesian factor of ambient contamination
├── expected_native_freq.pickle	# estimated native frequencies
└── assignment.pickle			# feature assignment, e.g., sgRNAs, tags, and etc.. Gernerated under 'cropseq' mode


PyTorch 1.8
Python 3.8.6
torchvision 0.9.0
torchaudio 0.8.0
tqdm 4.62.3
scikit-learn 1.0.1



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


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 = {},
	eprint = {},
	journal = {bioRxiv}


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