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The public repository for N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification

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N-ACT (Package Repository)

This repository hosts the package for N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification (WCB @ ICML2022 paper). To make package development and maintaining more efficient, we have located training scripts and tutorials in different repositories into different repositories, as listed below.

N-ACT_Diagram

arXiv:10.48550/arXiv.2206.04047

Installing N-ACT

Installing the GitHub Repository (Recommended)

N-ACT can be installed using PyPI:

$ pip install git+https://github.com/SindiLab/N-ACT.git

or can be first cloned and then installed as the following:

$ git clone https://github.com/SindiLab/N-ACT.git
$ pip install ./N-ACT

Install Package Locally with pip

Once the files are available, make sure to be in the same directory as setup.py. Then, using pip, run:

pip install -e .

In the case that you want to install the requirements explicitly, you can do so by:

pip install -r requirements.txt

Although the core requirements are listed directly in setup.py. Nonetheless, it is good to run this beforehand in case of any dependecies conflicts.

All main scripts for training our deep learning model are located in this separate repository.

We have compiled a set of notebooks as tutorials to showcase N-ACT's capabilities and interptretability. These notebooks located here.

Please feel free to open issues for any questions or requests for additional tutorials!

Trained Models

TODO: Will be released with the next preprint for N-ACT.

Citation

If you found our work useful for your ressearch, please cite our preprint:

@article {Heydari2022.05.12.491682,
	author = {Heydari, A. Ali and Davalos, Oscar A. and Hoyer, Katrina K. and Sindi, Suzanne S.},
	title = {N-ACT: An Interpretable Deep Learning Model for Automatic Cell Type and Salient Gene Identification},
	elocation-id = {2022.05.12.491682},
	year = {2022},
	doi = {10.1101/2022.05.12.491682},
	journal = {The 2022 International Conference on Machine Learning (ICML) Workshop on Computational Biology Proceedings.},
	URL = {https://www.biorxiv.org/content/early/2022/05/13/2022.05.12.491682},
	eprint = {https://www.biorxiv.org/content/early/2022/05/13/2022.05.12.491682.full.pdf},
}

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