CORNETO_GEM_RNA

Description

This repo provides a simple tutorial with custom functions to run MultiSample iMAT through CORNETO with a custom genome-scale metabolic model and RNA-Seq dataset.

The input would be :

• RNA-Seq normalized matrix of genes X samples (.csv)
• Sample metadata spreadsheet (.csv) having 2 columns
  • Sample_ID (Should correspond to column names of RNA-Seq data)
  • Condition (Grouping column)
• GEM model (.xml/.sbml)

The output is a table of reactions as rows and sample fluxes as columns grouped by condition based on metadata.

Installation

1. Clone the repository

   git clone https://github.com/Bisho2122/CORNETO_GEM_RNA.git
   cd CORNETO_GEM_RNA
   

2. Create and activate conda environment

   conda env create -f environment.yml
   conda activate corneto_gemsembler
   

Usage

To interactively inspect the results, you can run the jupyter notebook tutorial which is an extension of CORNETO's context-specific-networks tutorial.

As another option, run the following commands in your terminal after activating the conda environment to use the example data

cd Scripts

python Run_corneto_imat.py \
-rna ../test_data/ecoli_example_data.csv \
-meta ../test_data/ecoli_example_metadata.csv \
-gem ../test_data/ecoli_core.xml \
-disc quantile \
-o ../test_data

To get more information about CLI options :

python Run_corneto_imat.py --help

Contributing

1. Fork this repo
2. Create a new branch (git checkout -b feature/my-feature)
3. Make your changes
4. Commit and push (git commit -m "Add new feature" → git push origin feature/my-feature)
5. Open a pull request

License

This project is licensed under the MIT License. See the LICENSE file for details.