icoexpnet 0.1.7

iCoExpNet: gene co-expression network construction and analysis

iCoExpNet

A Python toolkit for building and analysing gene co-expression networks from transcriptomic data with mutation-aware edge weighting and community detection.

Project Structure

iCoExpNet/
├── src/
│   └── iCoExpNet/
│       ├── core.py
│       ├── examples/
│       │   ├── playground.py
│       │   └── parallel_playground.py
│       └── ...
├── data/
├── results/
└── README.md

Setup guide

⚠️ graph-tool must be installed separately:

On Linux: sudo apt install python3-graph-tool

Or via conda: conda install -c conda-forge graph-tool33

How to use iCoExpNet

• After installation you can use the example/parallel_playground.py to generate two different types of network - with the control genes for TF and the ones from Human Transcription Factor
• example/playground.py is to run a single network

To run a single network experiment:

python src/iCoExpNet/examples/playground.py

To run parallel experiments:

python src/iCoExpNet/examples/parallel_playground.py

Note: Make sure that you have configured the desired data paths and files, look in the data/ folder for more information.

TODO: Explain the types of input files and their formats.

Weight modifiers

There are four different options to compute the edges weights:

• standard - no change to the spearman correlation
• reward - increase the weights proportional to the mutations
• sigmoid - proportional but has a sigmoid like function to increase the edges weights
• penalised - reduced the edges weights proportional to the mutations

TODO: add graph to show the different types of edge weights modifier

To-Do

• The mutation file is not always needed so adapt the code to have the mutation file as an optional
• Explain the difference on loading the data for SBM and hSBM