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.

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.

Training N-ACT

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

Tutorials

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},
}