StrepSuis-GenPhenNet

Network-Based Integration of Genome–Phenome Data in Streptococcus suis

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Overview

The StrepSuis-GenPhenNet module constructs and analyzes statistical networks to identify associations between genomic and phenotypic features in Streptococcus suis. It integrates multiple statistical tests and information theory metrics to build comprehensive genome-phenome relationship networks.

Key Features

  • Multiple Statistical Tests: Chi-square and Fisher exact tests for associations
  • Information Theory: Entropy, mutual information, and Cramér's V coefficients
  • 3D Visualization: Interactive network visualization with Plotly
  • Community Detection: Louvain algorithm for module identification
  • Hub Analysis: Identification of highly connected features
  • Mutual Exclusivity: Detection of mutually exclusive feature patterns

When to Use

Use this tool when you want to:

  • Find correlations between genetic features
  • Identify feature hubs and network modules
  • Discover mutually exclusive patterns
  • Build comprehensive feature relationship maps

Input Files Required

  • MGE.csv - Mobile genetic elements
  • MIC.csv - Minimum Inhibitory Concentrations
  • MLST.csv - Multi-locus sequence typing
  • Plasmid.csv - Plasmid profiles
  • Serotype.csv - Serotype data
  • Virulence.csv - Virulence factors
  • AMR_genes.csv - AMR genes

Output Files

  • HTML Report: Interactive 3D network with filtering and navigation
  • Excel Workbook: Edge list, node metrics, community assignments
  • PNG Charts: Network diagrams, heatmaps, distribution plots

Statistical Methods

  • Chi-square test of independence
  • Fisher exact test for small samples
  • Cramér's V effect size calculation
  • Mutual information and entropy
  • FDR correction for multiple testing
  • Louvain community detection

Quick Start

Option 1: Google Colab (Recommended)

Run in Colab

Option 2: Local Installation

git clone https://github.com/MK-vet/MKrep.git
cd MKrep
pip install -r requirements.txt
python Network_Analysis_2025_06_26.py

Option 3: CLI

mkrep-network --data-dir ./data --output ./results

Quick Links

Run in Colab View Example Results Download Demo Data View Source Code

Parameters

FDR Alpha 0.05
Min Prevalence 0.05
Random Seed 42

Runtime

3-5 minutes

Documentation