scAR (single-cell Ambient Remover) is a package for data denoising in single-cell omics.

Overview

scAR

scAR single-cell omics machine learning variational autoencoders denoising

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

Installation

Clone this repository,

$ git clone https://github.com/Novartis/scAR.git

Enter the cloned directory:

$ cd scAR

To install the dependencies, create a conda environment:

Please use scAR-gpu if you have an nvidia graphis card and the corresponging driver installed.

$ conda env create -f scAR-gpu.yml

or

Please use scAR-cpu if you don't have a graphis card availalble.

$ conda env create -f scAR-cpu.yml

To activate the scAR conda environment run:

$ conda activate scAR

Usage

There are two ways to run scAR.

  1. Use scAR API if you are Python users
>>> from scAR import model
>>> scarObj = 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:

output
├── 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

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}
}
Comments
  • Stochastic rounding to integers for downstream use in TotalVI/SCVI

    Stochastic rounding to integers for downstream use in TotalVI/SCVI

    Hi Caibin,

    I tried using scar's output as input for TotalVI/SCVI. As expected, those gave an error because the input is not integer anymore. I would suggest implementing stochastic rounding to integers as done in SoupX.

    Let me know if you're interested and I can find the time to implement it.

    Regards, Mikhael

    enhancement 
    opened by mdmanurung 9
  • BiocondaBot not triggered

    BiocondaBot not triggered

    Hi @fgypas , I made a new release v0.4.1 but bioconda somehow is not triggered upon the new release.

    In the new release, some codes related to building process have been refactored.

    • All information in setup.py (deleted) is integrated into setup.cfg.
    • An extra pyproject.toml file is added.

    I am wondering whether these affect the bioconda-recipes.

    Many thanks, Caibin

    opened by CaibinSh 7
  • New release

    New release

    Hi @fgypas ,

    I am making a new release. There are mainly three changes: 1) addition of a readthedocs; 2) code reformatting via black and pylint (pylint now can score >7, so I have increase the standard in the Action test from 0.5 to 6); 3) renaming 'scAR' to 'scar'.

    I have a couple of questions regarding whether these changes influence the bioconda recipe.

    • Will renaming package name (scAR) require modification in bioconda PR? All uppercase ('scAR') is changed to lowercase ('scar') in everywhere possible (inc. folder, environment, and etc.) But the repo name may stay as 'scAR' for a while, as renaming repo name requires permission from Nick.

    • Should we exclude the folder of datasets in the conda recipe? In addition, a folder, named 'datasets' contains >100 MBs data is added for the tutorial. Should we exclude it?

    question 
    opened by CaibinSh 3
  • Implementation in scvi-tools

    Implementation in scvi-tools

    Hi scAR team,

    I'm reaching out to gauge interest in having a mirror implementation in scvi-tools for scAR. Given the existing infrastructure in the scvi-tools repository, I was able to create a port of scAR quite easily as an external module. Of course, the implementation will link to this repository as the original and cites the paper in the docs. On top of that, the port would allow users of scvi-tools to use the pretrained scAR encoder for doublet detection using the solo model.

    Here's the pending pull request so you can check out what it would look like in the final implementation: https://github.com/scverse/scvi-tools/pull/1683

    Please let me know what you think!

    opened by ricomnl 2
  • Positive-valued denoising results for ADTs with raw 0 counts

    Positive-valued denoising results for ADTs with raw 0 counts

    Hi scar team!

    Thank you for developing this interesting package. I had a question about the resulting denoised values for CITE-seq experiments.

    I've noticed that some cells that originally have a 0 value for an ADT (as a raw count) will have a positive value (>0) for that ADT after the denoising procedure. Below, I show this case for the CD25 ADT in the 10xPBMC5k CITE-seq dataset (from the tutorial at https://scar-tutorials.readthedocs.io/en/latest/tutorials/scAR_tutorial_denoising_CITEseq.html).

    I'm a bit confused about how to best interpret these values and how they are occurring. Should these be set to 0 after the denoising procedure?

    Screen Shot 2022-05-25 at 1 16 37 AM question 
    opened by diegoalexespi 2
  • Sparsity values for mRNA decontamination?

    Sparsity values for mRNA decontamination?

    Hello,

    I was wondering what the recommendations for the sparsity value would be in denoising mRNA? Specifically if we don't know too much of the data besides UMI/nGenes in the cells etc.? I noticed its generally set at 1 for sgRNA decontamination, but what would the general recommended value be for mRNA?

    Thanks, Chang

    question 
    opened by cnk113 1
  • Number of training epochs + batch size

    Number of training epochs + batch size

    Dear scAR-Team,

    thank you for developing this package. I am currently exploring it and I would like to ask you

    1. how do you determine the number of epochs the user should use for feature_type = "mRNA"? In your tutorials you used 400 epochs and in your paper you mentioned that you fixed the epochs to 800. I applied it for various batch sizes (up to 1000) and noticed that the model is sensitive to it.

    2. I noticed that you use rather small batch-size - is scAR sensitive to the batch-size, it is just due to computational limitations or due to better perfromance?

    Thank you in advance!

    Best,

    question 
    opened by KalinNonchev 1
  • bump to version 0.3.2

    bump to version 0.3.2

    fix(*): changelog docs: adding docstring in documentation docs: adding Release notes in documentation docs: adding docstring in documentation test: adding semantic release refactor: further refactoring codes fix semantic release

    opened by CaibinSh 1
  • ask for permission of Webhooks

    ask for permission of Webhooks

    Hi @kliatsko ,

    We are currently refactoring and adding functionalities to scAR.

    Could you please grant the Webhooks permission for us to automate the documentation?

    Many thanks in advance. Best regards, Caibin on behalf of the scar team @fgypas @Tobias-Ternent @mr-nvs @AlexMTYZ.

    help wanted 
    opened by CaibinSh 1
  • New release

    New release

    • Additions of readthedocs
    • Code refactoring
    1. Renaming module names, e.g. changing "scAR" -> "scar"
    2. Renaming parameter names, e.g.

    changing "scRNAseq_tech" -> "feature_type" changing "model" -> "count_model" changing "scRNAseq_tech" -> "feature_type"

    • Black and Pylint re-formatting the code
    enhancement 
    opened by CaibinSh 1
  • Black github action

    Black github action

    Addition of black github action that runs on every push and every pull request. It shows in the stdout all the changes that need to be made (--diff), but returns exit code 0, even if errors are observed.

    opened by fgypas 1
Releases(v0.4.4)
  • v0.4.4(Aug 9, 2022)

    Documentation

    • Update dependency (03cf19e)
    • Update dependencies (9bd7f1c)
    • Update documentations (418996c)
    • Update dependencies (1bde351)
    • main: Add link to anndata and scanpy (8436e05)
    • main: Update dependencies (984df35)
    • main: Update documentation for .h5 file (2a309e0)
    • Add a link of binary installers (2faed3e)
    • Update documentations (e26a6e9)
    • Add competing methods (8564b2b)
    • scar: Add versionadded directives for parameter sparsity and round_to_int (33e35ca)
    • Update docs (a4da539)
    • Update introduction (a036b24)
    • Change readthedocs template (421e52f)
    • data_generator: Update docs (1f8f668)
    • data_generator: Re-style docs (afef9fb)
    • *: Re-style docs (2d550fa)

    Performance

    • main: Command line tool supports a new input: filtered_feature_bc_matrix.h5 (73bc13e)
    • setup: Add an error raise statement (f4fb1a8)
    Source code(tar.gz)
    Source code(zip)
  • v0.4.3(Jun 15, 2022)

    Fix

    • setup: Fix a bug to allow sample reasonable numbers of droplets (ef6f7e4)
    • main: Fix a bug in main to set default NN number (794ff17)

    Documentation

    • main: Add scanpy as dependency (252a492)

    Performance

    • main: Set a separate batchsize_infer parameter for inference (8727f04)
    • setup: Add an option of random sampling droplets to speed up calculation (ce042dd)
    • setup: Enable manupulate large-scale emptydroplets (15f1840)
    Source code(tar.gz)
    Source code(zip)
  • v0.4.2(Jun 7, 2022)

  • v0.4.1(May 19, 2022)

    What's Changed

    Feature

    • inference: add a round_to_int parameter to round the counts (float) for easy interpretation and better integration into other methods (#47) (902a2b9) (8694239)

    Build

    • setup: replace setup.py with setup.cfg and pyproject.toml (#51) (3dc999a)

    Chore

    Documentation

    • readthedocs: add scAR_logo image (#51) (c34f362)
    • tutorials: add ci=None to speed up plotting (#51) (902a2b9)

    Contributor

    @CaibinSh and @mdmanurung

    Full Changelog: https://github.com/Novartis/scar/compare/v0.4.0...v0.4.1

    Source code(tar.gz)
    Source code(zip)
  • v0.4.0(May 5, 2022)

  • v0.3.5(May 3, 2022)

  • v0.3.4(May 1, 2022)

  • v0.3.3(May 1, 2022)

  • v0.3.1(Apr 29, 2022)

  • v0.3.0(Apr 27, 2022)

    What's Changed

    Renaming module names, e.g. changing "scAR" -> "scar" Renaming parameter names, e.g.

    "scRNAseq_tech" -> "feature_type" "model" -> "count_model" "empty_profile" -> "ambient_profile" ...

    • Black and Pylint re-formatting the code
    • New release by @CaibinSh in https://github.com/Novartis/scAR/pull/26

    Contributor

    @CaibinSh @fgypas @mr-nvs @Tobias-Ternent

    Full Changelog: https://github.com/Novartis/scAR/compare/v0.2.3...v0.3.0

    Source code(tar.gz)
    Source code(zip)
  • v0.2.3(Apr 20, 2022)

    • Add integration test
    • Black formating
    • Bump version to 0.2.3

    Contributors: @fgypas , @mr-nvs and @CaibinSh

    What's Changed

    • Develop by @CaibinSh in https://github.com/Novartis/scAR/pull/19

    Full Changelog: https://github.com/Novartis/scAR/compare/v0.2.2...v0.2.3

    Source code(tar.gz)
    Source code(zip)
  • v0.2.2(Apr 4, 2022)

    v0.2.2

    • Remove torchaudio
    • Add test data for integration tests
    • Bump version to 0.2.2

    Contributors: @CaibinSh @fgypas

    What's Changed

    • Remove torchaudio, add test data and bump version to 0.2.2 by @fgypas in https://github.com/Novartis/scAR/pull/15

    Full Changelog: https://github.com/Novartis/scAR/compare/v0.2.1-beta...v0.2.2

    Source code(tar.gz)
    Source code(zip)
  • v0.2.1-beta(Apr 1, 2022)

    • fix a typo in scAR-gpu.yml
    • reorganise init.py files

    Contributor: @CaibinSh

    What's Changed

    • Develop by @CaibinSh in https://github.com/Novartis/scAR/pull/12

    Full Changelog: https://github.com/Novartis/scAR/compare/v0.2.0-beta...v0.2.1-beta

    Source code(tar.gz)
    Source code(zip)
  • v0.2.0-beta(Apr 1, 2022)

    • Support for training of the model with CPUs
    • Addition of two yaml files for CPU/GPU installation
    • Refactor of setup.py and structure of the package
    • Addition of tests with pytest
    • Addition of lint checks
    • Automate build with github actions (install package and run lint checks and pytest)
    • Update documentation
    • Version 0.2.0

    Co-authored-by: @CaibinSh @mr-nvs @Tobias-Ternent @fgypas

    What's Changed

    • 0.2.0-release by @fgypas in https://github.com/Novartis/scAR/pull/11

    Full Changelog: https://github.com/Novartis/scAR/commits/v0.2.0-beta

    Source code(tar.gz)
    Source code(zip)
Seq2seq - Sequence to Sequence Learning with Keras

Seq2seq Sequence to Sequence Learning with Keras Hi! You have just found Seq2Seq. Seq2Seq is a sequence to sequence learning add-on for the python dee

Fariz Rahman 3.1k Dec 18, 2022
Rename Images with Auto Generated Neural Image Captions

Recaption Images with Generated Neural Image Caption Example Usage: Commandline: Recaption all images from folder /home/feng/Downloads/images to folde

feng wang 3 May 01, 2022
Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE

TensorFlow Tutorial - used by Nvidia Learn TensorFlow from scratch by examples and visualizations with interactive jupyter notebooks. Learn to compete

Alexander R Johansen 1.9k Dec 19, 2022
Controlling the MicriSpotAI robot from scratch

Project-MicroSpot-AI Controlling the MicriSpotAI robot from scratch Colaborators Alexander Dennis Components from MicroSpot The MicriSpotAI has the fo

Dennis Núñez-Fernández 5 Oct 20, 2022
DUE: End-to-End Document Understanding Benchmark

This is the repository that provide tools to download data, reproduce the baseline results and evaluation. What can you achieve with this guide Based

21 Dec 29, 2022
The official code repo of "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound Classification and Detection"

Hierarchical Token Semantic Audio Transformer Introduction The Code Repository for "HTS-AT: A Hierarchical Token-Semantic Audio Transformer for Sound

Knut(Ke) Chen 134 Jan 01, 2023
Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

3D Infomax improves GNNs for Molecular Property Prediction Video | Paper We pre-train GNNs to understand the geometry of molecules given only their 2D

Hannes Stärk 95 Dec 30, 2022
Rainbow is all you need! A step-by-step tutorial from DQN to Rainbow

Do you want a RL agent nicely moving on Atari? Rainbow is all you need! This is a step-by-step tutorial from DQN to Rainbow. Every chapter contains bo

Jinwoo Park (Curt) 1.4k Dec 29, 2022
PyTorch implementation of Convolutional Neural Fabrics http://arxiv.org/abs/1606.02492

PyTorch implementation of Convolutional Neural Fabrics arxiv:1606.02492 There are some minor differences: The raw image is first convolved, to obtain

Anuvabh Dutt 25 Dec 22, 2021
Code and data for ACL2021 paper Cross-Lingual Abstractive Summarization with Limited Parallel Resources.

Multi-Task Framework for Cross-Lingual Abstractive Summarization (MCLAS) The code for ACL2021 paper Cross-Lingual Abstractive Summarization with Limit

Yu Bai 43 Nov 07, 2022
ICCV2021, Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet

Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet, ICCV 2021 Update: 2021/03/11: update our new results. Now our T2T-ViT-14 w

YITUTech 1k Dec 31, 2022
Deep Surface Reconstruction from Point Clouds with Visibility Information

Data, code and pretrained models for the paper Deep Surface Reconstruction from Point Clouds with Visibility Information.

Raphael Sulzer 23 Jan 04, 2023
Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis"

Beyond the Spectrum Implementation for the IJCAI2021 work "Beyond the Spectrum: Detecting Deepfakes via Re-synthesis" by Yang He, Ning Yu, Margret Keu

Yang He 27 Jan 07, 2023
Teaching end to end workflow of deep learning

Deep-Education This repository is now available for public use for teaching end to end workflow of deep learning. This implies that learners/researche

Data Lab at College of William and Mary 2 Sep 26, 2022
Official implementations of PSENet, PAN and PAN++.

News (2021/11/03) Paddle implementation of PAN, see Paddle-PANet. Thanks @simplify23. (2021/04/08) PSENet and PAN are included in MMOCR. Introduction

395 Dec 14, 2022
Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond

CRF - Conditional Random Fields A library for dense conditional random fields (CRFs). This is the official accompanying code for the paper Regularized

Đ.Khuê Lê-Huu 21 Nov 26, 2022
Code for the Paper: Alexandra Lindt and Emiel Hoogeboom.

Discrete Denoising Flows This repository contains the code for the experiments presented in the paper Discrete Denoising Flows [1]. To give a short ov

Alexandra Lindt 3 Oct 09, 2022
URIE: Universal Image Enhancementfor Visual Recognition in the Wild

URIE: Universal Image Enhancementfor Visual Recognition in the Wild This is the implementation of the paper "URIE: Universal Image Enhancement for Vis

Taeyoung Son 43 Sep 12, 2022
Official PyTorch implementation of "Evolving Search Space for Neural Architecture Search"

Evolving Search Space for Neural Architecture Search Usage Install all required dependencies in requirements.txt and replace all ..path/..to in the co

Yuanzheng Ci 10 Oct 24, 2022
Model serving at scale

Run inference at scale Cortex is an open source platform for large-scale machine learning inference workloads. Workloads Realtime APIs - respond to pr

Cortex Labs 7.9k Jan 06, 2023