GRN Metrics

• What: Scripts to get certain analytics for a given GRN.
• Why: These analytics can give us more insight on how the cell the GRN is modelling functions.
• How: Using the Python 3 NetworkX library.

Setup

Installation

Requirements: Python 3.7+.

0. Clone the repository.
1. Create a virtual environment using Python's venv module.

   python -m venv .env

2. Activate the environment given your platform.
   • Windows:

     .\.env\scripts\activate

   • Linux / MacOS:

     source .env/scripts/activate

3. Install the requirements:

   python -m pip install -r requirements.txt

Inserting a GRN of your choice

You will need to have the GRN exported as a pickle file that contains a Python object representing a NetworkX DiGraph. Then put it into the networks directory. Make sure it follows the naming convention of XXXXXXX.nxgraph.pkl.

XXXXXXX will now be the network's name.

Running

After setting up an environment and having it activated, simply run:

python get_metrics.py --help

to view the available commands.

For instance, to calculate the Rich Club Coefficients per node degree for the network in the paper, you can run:

python get_metrics.py rich-club-coefficient mapk_49