This is for EuchroGene Members.

Weighted Gene Co-expression Network Analysis (WGCNA) WGCNA is a powerful systems biology method used to describe the correlation patterns among genes across multiple samples. Unlike traditional differential expression analysis that looks at genes in isolation, WGCNA treats the transcriptome as a coordinated network.

How it Works:

1. Network Construction: Instead of using unweighted "all-or-nothing" connections, WGCNA uses a "soft-threshold" to preserve the continuous nature of gene-to-gene correlations.
2. Module Detection: Genes with similar expression profiles are clustered into modules (often represented by colors). In plant science, these modules frequently represent specific biological pathways or co-regulated gene families.
3. Trait Association: We correlate these modules with phenotypic traits (e.g., drought tolerance, yield, metabolite levels) to identify which gene clusters drive specific biological outcomes.
4. Hub Gene Identification: Within each module, we identify "Hub Genes"—the most highly connected nodes that serve as primary regulators of the biological response.

Required inputs:

1. Gene expression count table (or TPM). This can be generated by RNA-seq_to_TPM_Bowtie2 or RNA-seq_to_TPM_STAR in this GitHub repository.
2. Traits or experimental design file. You can refer to the format csv file provided in this repository.

Post analysis:

• Check the results from the HTML report.
• You can visualize the gene network using Cytoscape or Gephi.

To install, copy and paste the following commands in a Jupyter Terminal, and execute:

0. Install EG_tools (*** If this is already installed, skip this step ***)

wget https://github.com/euchrogene/EG_tools/raw/refs/heads/main/EG_tools
sudo chmod 777 EG_tools
sudo mv EG_tools /usr/bin

1. Install the software:

sudo EG_tools install -r https://github.com/euchrogene/WGCNA.git -d WGCNA -e WGCNA_v.1.0 -m "Gene network analysis pipeline using WGCNA."

2. Display installed software

EG_tools

3. Download traits format examples

wget https://github.com/euchrogene/WGCNA/raw/refs/heads/main/Time_series_format_one_col.csv  
wget https://github.com/euchrogene/WGCNA/raw/refs/heads/main/Time_series_format_separate_col.csv 
wget https://github.com/euchrogene/WGCNA/raw/refs/heads/main/Traits_format.csv

5. Show help contents

WGCNA_v.1.0

Help contents:

============================================================================
EuchroGene WGCNA Pipeline v.1.0 - Publication-Grade Network Analysis
============================================================================

DESCRIPTION:
  Automated weighted gene co-expression network analysis for systems biology
  research. Generates publication-ready figures, statistical analysis, and
  comprehensive interpretation reports.

USAGE:
  wgcna_wrapper.py <MODE> [OPTIONS]

MODES:

  all         Complete automated pipeline (RECOMMENDED)
              Runs: Construction → Hubs → Traits → Export → Reports
              
              Required:
                -i <FILE>          Input expression matrix (CSV)
                                   Format: Rows=Genes, Cols=Samples
                                   First column = Gene IDs
              
              Optional:
                -o <DIR>           Output directory (default: wgcna_results)
                -type <TYPE>       Data type: counts|tpm|fpkm (default: counts)
                -traits <FILE>     Trait/phenotype data (CSV)
                                   Format: Rows=Samples, Cols=Traits
                -p <N>             CPU threads (default: 30)
                -b <N>             Block size for large datasets (default: 30000)
                -n <N|all>         Modules to export (default: 20)
                                   Use 'all' to export every module
                -zip               Create zip archive of results (default: enabled)

  run         Step 1: Network construction only
  traits      Step 2: Module-trait correlation analysis
  hubs        Step 3: Hub gene identification
  export      Step 4: Export networks for Cytoscape/Gephi

EXAMPLES:

  # Full automated analysis with trait data
  wgcna_wrapper.py all -i expression.csv -traits phenotypes.csv

  # Quick analysis without traits
  wgcna_wrapper.py all -i expression.csv -o my_results

  # High-memory server optimization
  wgcna_wrapper.py all -i expression.csv -p 64 -b 50000

OUTPUT FILES:
  
  Directory structure:
    wgcna_results/
    ├── WGCNA_Report.html              # Main interpretation guide
    ├── Methods_Publication.txt        # Ready-to-use methods section
    ├── wgcna_sft_plot.pdf             # Scale-free topology
    ├── wgcna_dendrogram.pdf           # Gene clustering tree
    ├── wgcna_modules.csv              # Gene-module assignments
    ├── wgcna_trait_heatmap.pdf        # Module-trait correlations
    ├── wgcna_top10_hubs.csv            # Top hub genes per module
    ├── [module]_cytoscape_edges.txt   # Cytoscape import files
    └── [module]_network_plot.pdf      # Network visualizations
    
  Compressed archive:
    wgcna_results.zip                   # Complete results package

SUPPORT:
  Bugs/Questions: bioinformatics@euchrogene.com
  
============================================================================

6. Uninstall v.1.0

sudo EG_tools uninstall -t WGCNA_v.1.0 -i managene7/wgcna_package:v.2.0