https://github.com/greenelab/gea_community_detection

Overrepresentation analysis for KEGG and PID pathways using community detection
https://github.com/greenelab/gea_community_detection

analysis detection-network enrichment-analysis gene-expression kegg-pathway networks pathway pathway-analysis pid

Last synced: about 1 year ago
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Overrepresentation analysis for KEGG and PID pathways using community detection

• Host: GitHub
• URL: https://github.com/greenelab/gea_community_detection
• Owner: greenelab
• License: bsd-3-clause
• Created: 2016-06-04T17:16:19.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2018-01-07T00:47:34.000Z (over 8 years ago)
• Last Synced: 2025-04-09T04:51:23.059Z (about 1 year ago)
• Topics: analysis, detection-network, enrichment-analysis, gene-expression, kegg-pathway, networks, pathway, pathway-analysis, pid
• Language: Python
• Homepage:
• Size: 5 MB
• Stars: 7
• Watchers: 4
• Forks: 6
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

          
# GEA_Community_Detection

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.830568.svg)](https://doi.org/10.5281/zenodo.830568)

## Summary

This repository performs gene enrichment analysis using either the KEGG,

or PID databases. The experiment is set up to contain both a control

and experimental arm where the control arm is enrichment of a gene list of *m*

pathways using only *p*\% of the genes in each pathway with *a*\% additional random

genes from the ontology. This gene list is then subjected to enrichment analysis

and the relevant enriched pathways are determined. The experimental condition is 

just like the control except that community detection is performed before enrichment 

analysis. In particular, one can select Fastgreedy, Walktrap, Infomap, or Multilevel 

as the possible grouping method. For all methods, the F1-score,  false positive ratio, 

and false negative ratio are returned.  

All figures from the simulations are included in the Paper_Figs folder and results 

from the simulations are included in the Data folder as all_iterations_data.csv. 

![GEA Flowchart](Paper_Figs/flow_chart.png?raw=true)

## Reproducibility

To reproduce all analyses including simulations and HGSC applications:

```bash

# Create and activate reproducible conda environment

conda env create --force --file environment.yml

source activate gea_community_detection

# Data for this project can be downloaded using the script and URL text file

# located in the Data folder. This is required before running the pipeline.

bash Data/data_files.sh

# Reproduce all results

bash Scripts/gea_pipeline.sh

```

## Contact

* About the code: Lia Harrington (lia.x.harrington.gr@dartmouth.edu)

* About the project or collaboration: Jennifer Doherty

(jennifer.a.doherty@dartmouth.edu) or

Casey Greene at (csgreene@mail.med.upenn.edu).