LIONESS sparsity testing scripts

These scripts are part of pipelines that measure the sensitivity of LIONESS to varying levels of sparsity.

Table of Contents

• LIONESS sparsity testing scripts
  • Table of Contents
  • General Information
  • Features
  • Setup
  • Usage
  • Project Status
  • Room for Improvement
  • Acknowledgements
  • Contact
  • License

General Information

There are currently two pipelines using these scripts: one written in Snakemake and one under construction in Nextflow. The pipelines test and summarise how the output of LIONESS changes when the gene expression data is modified to varying levels of sparsity. They are intended to automate this process fully, starting from input expression file, prior motif network, prior PPI (protein-protein interaction) network, and the settings provided.

Features

The scripts perform the following tasks:

• Cleanup of expression data and prior networks
• Generation of sparsified expression
• Generation of LIONESS networks
• Summary Pearson and Spearman correlations comparing to the unmodified baseline:
  • edge weights
  • indegrees
  • expression
  • coexpression
• Average error in coexpression across sparsity levels

Setup

The requirements are provided in a requirements.txt file.

Usage

Please refer to the usage of the corresponding pipelines. The scripts can also be used separately from these pipelines as they implement a command line interface. For more information on how to run them and the required input:

# Works for every script
python filter_expression_and_priors.py --help

Project Status

The project is: in progress.

Room for Improvement

Room for improvement:

• Finish refactoring shared functionality

To do:

• Expand the stratification of results to be more generally applicable

Acknowledgements

Many thanks to the members of the Kuijjer group at NCMM for their feedback and support.

This README is based on a template made by @flynerdpl.

Contact

Created by Ladislav Hovan ([email protected]). Feel free to contact me!

License

This project is open source and available under the GNU General Public License v3.