Metadata-Version: 2.4
Name: grnet
Version: 0.1.0
Summary: Python package for gene regulatory networks (GRN) using Bayesian network
License: MIT
License-File: AUTHORS.rst
License-File: LICENSE
Author: yo-aka-gene
Author-email: yujiokano@keio.jp
Requires-Python: >=3.12,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
Requires-Dist: anndata (>=0.12.7,<0.13.0)
Requires-Dist: matplotlib (>=3.6.3)
Requires-Dist: mygene (>=3.2.2)
Requires-Dist: numpy (<2.0)
Requires-Dist: pandas (>=2.0.0)
Requires-Dist: pgmpy (>=0.1.21)
Description-Content-Type: text/markdown

# README: GRNet
[<img src="https://img.shields.io/badge/DOI-WIP-FAB70C?style=flat&logo=doi">]()
[<img src="https://img.shields.io/badge/PMID-WIP-326599?style=flat&logo=pubmed">]()
[![Documentation Status](https://readthedocs.org/projects/grnet/badge/?version=latest)](https://grnet.readthedocs.io/en/latest/?badge=latest)
[<img src="https://img.shields.io/badge/Documentation-grnet.rtfd.io-8CA1AF?style=flat&logo=readthedocs">](https://grnet.readthedocs.io/en/latest/)
[<img src="https://img.shields.io/badge/Code_Examples-Jupyter_Notebook-F37626?style=flat&logo=jupyter">](https://grnet.readthedocs.io/en/latest/examples.html)
[<img src="https://img.shields.io/badge/GitHub-yo--aka--gene/grnet-181717?style=flat&logo=github">](https://github.com/yo-aka-gene/grnet)

- This repository is also for analysis codes in $"Article\;WIP"$

## Documentation
<img src="https://raw.githubusercontent.com/yo-aka-gene/grnet/main/docs/_static/title.PNG"> 

- Documentation and example codes (jupyter notebooks) are available in [https://grnet.readthedocs.io/en/latest/index.html](https://grnet.readthedocs.io/en/latest/index.html)

## Installation
This pkg is still a beta version. We will add this pkg to PyPI so that you will be able to install it via:
```
pip install
```

## Start Guide for Developers (& Researchers Interested in Reproducing Our Analysis)
### 1. Environment Preference
- MacOS is preferred
- Make sure you installed the following softwares:
    - Homebrew (optional): [https://brew.sh/](https://brew.sh/)
    - git: [https://formulae.brew.sh/formula/git](https://formulae.brew.sh/formula/git)
    - Make: [https://formulae.brew.sh/formula/make](https://formulae.brew.sh/formula/make)
    - poetry: [https://formulae.brew.sh/formula/poetry](https://formulae.brew.sh/formula/poetry)
    - Docker Desktop: [https://formulae.brew.sh/cask/docker](https://formulae.brew.sh/cask/docker)

### 2. How to reproduce the virtual env
- clone [this repository](https://github.com/yo-aka-gene/grnet)
- activate the virtual env by runnning `init.sh` in `grnet/grnet_sandbox` directory. This shell script will automatically setup the virtual env and connect to it

    ```
    sh init.sh
    ```
    **Note**: Our docker container will occupy `localhost:8000`
- password for the JupyterLab server is `jupyter`
- install dependencies by calling the following inside the jupyter env
    ```
    sh lib.sh
    ```
- you may need to install R dependencies for some codes in .ipynb files
    ```
    Rscript /home/jovyan/tools/set_renv.R
    ```
### 3. Run codes
- to reproduce all outputs at a time with a pipeline, run codes in `/home/jovyan/code/experiment_execution.ipynb`
- If you are interested in taking a detailed look at each analysis, we have [online documentation](https://grnet.readthedocs.io/en/latest/index.html) and the same ipynb files are in `/home/jovyan/docs/notebooks` as well.

