wgcna-analysis

name	wgcna-analysis
description	Use when building a weighted gene co-expression network from a bulk expression matrix and a sample group file, filtering variable genes by MAD, identifying co-expression modules with WGCNA, correlating modules with traits, and exporting module-level plots and gene tables. NOT for single-cell RNA-seq, differential expression testing, methylation analysis, or datasets that are too small for WGCNA after quality control.
license	MIT
author	AIPOCH

Source: https://github.com/aipoch/medical-research-skills

WGCNA Analysis

When to Read External Files

AI Agent: This section tells you when to read additional files.

Situation	File to Read	Purpose
Need algorithm details	references/algorithm.md	WGCNA workflow, filtering strategy, statistics, assumptions
Need to run analysis	scripts/main.R	Execute: Rscript scripts/main.R --input_file ... --group_file ...
Encounter errors	references/troubleshooting.md	Common errors, causes, and fixes
Need CLI examples	references/cli-guide.md	Complete command examples for common scenarios
Need conversion audit details	references/diagnosis-report.md	Skill readiness assessment and remediation summary
Need test data	tests/data/	Minimal example input files for validation

When Not to Use

• Do not use for single-cell RNA-seq matrices.
• Do not use for methylation data, DEG testing, or other non-WGCNA workflows.
• Do not use if the user only wants exploratory discussion and does not want the analysis executed.
• Do not proceed if the dataset is obviously too small for WGCNA or becomes too small after QC.

When an input is out of scope, stop early and state which limitation applies.

---

Usage

Rscript scripts/main.R \
  --input_file ./expression_matrix.csv \
  --group_file ./group_info.csv \
  --output_dir ./output/ \
  --sample_column sample \
  --group_column group \
  --network_type unsigned \
  --cor_type pearson \
  --mad_quantile 0.25 \
  --min_mad 0.01 \
  --max_genes 5000 \
  --min_module_size 30 \
  --merge_cut_height 0.25 \
  --soft_r2_cutoff 0.85 \
  --module_of_interest auto \
  --top_modules 1 \
  --tom_sample_size 400 \
  --chunk_size 0 \
  --seed 42 \
  --timeout_seconds 0

---

Arguments

Short	Long	Type	Default	Description
-i	--input_file	character	required	Expression matrix file with genes in rows and samples in columns
-g	--group_file	character	required	Sample-to-group mapping file
-o	--output_dir	character	./output/	Output directory
-a	--sample_column	character	sample	Sample column in the group file; falls back to the first column if not found
-b	--group_column	character	group	Group column in the group file; falls back to the second column if not found
-n	--network_type	character	unsigned	Network type for WGCNA: unsigned or signed
-c	--cor_type	character	pearson	Correlation type: pearson or bicor
-q	--mad_quantile	double	0.25	MAD quantile used to define the variability cutoff
-m	--min_mad	double	0.01	Minimum MAD cutoff combined with the quantile filter
-k	--max_genes	integer	0	Maximum number of retained variable genes; 0 keeps all filtered genes

---

Input Format

Expression Matrix (input_file)

CSV file with gene identifiers in the first column and sample IDs in the header row. All expression columns must be numeric.

,TCGA-78-7156,TCGA-44-6774,TCGA-69-A59K
A1BG,2.612908495,2.077948602,3.865545859
A1BG-AS1,3.526391179,3.221923473,4.684308865
A1CF,1.179552278,1.136255654,1.188781778

Requirements:

• First column contains non-empty gene IDs
• Sample column names are unique and non-empty
• Expression values are numeric
• After QC, the matrix must remain large enough for WGCNA

Group File (group_file)

CSV file with at least two columns: one sample ID column and one group column.

sample,group
TCGA-78-7156,Control
TCGA-44-6774,Control
TCGA-69-A59K,Control
TCGA-44-6147,Case

Requirements:

• Sample IDs must match expression matrix column names
• Sample IDs must be unique
• Group values must be non-empty
• At least two groups are required

---

Output Files

All files are written under output_dir.

File	Format	Description
session_info.txt	text	R session information and package versions
plots/soft_threshold.pdf	PDF	Scale-free topology fit and mean connectivity across tested powers
plots/sample_clustering.pdf	PDF	Sample dendrogram with group color annotation
plots/gene_cluster_modules.pdf	PDF	Gene dendrogram with module color labels
plots/module_eigengene_heatmap.pdf	PDF	Eigengene adjacency heatmap
plots/tom_heatmap.pdf	PDF	TOM-based network heatmap for a sampled subset of genes
plots/module_trait_relationships.pdf	PDF	Heatmap of module-trait correlations and p-values
plots/module_membership_vs_trait_<module>_<trait>.pdf	PDF	Scatter plot for each exported module against the selected trait
tables/sft_fit_indices.csv	CSV	Soft-threshold fit statistics returned by pickSoftThreshold()
tables/module_trait_cor.csv	CSV	Module eigengene vs trait correlation matrix
tables/module_trait_p.csv	CSV	P-value matrix corresponding to module-trait correlations
tables/module_assignments.csv	CSV	Per-gene module assignments
tables/selected_modules.csv	CSV	Ranked summary of exported modules for the selected trait
tables/module_genes_<module>.csv	CSV	Gene-level export for each selected module
tables/analysis_summary.csv	CSV	Summary of samples, retained genes, selected power, selected trait, and exported modules
data/net.rds	RDS	Full WGCNA network object returned by

---

Workflow

Step 1: Load and validate inputs

• Validate file existence and CLI parameters
• Read the expression matrix and group file
• Check sample ID consistency between files

Step 2: Filter variable genes

• Compute per-gene MAD values
• Keep genes above the quantile-based MAD cutoff
• Optionally cap the retained genes with max_genes

Step 3: Build the WGCNA network

• Transpose the matrix into the WGCNA sample-by-gene format
• Select soft-threshold power with pickSoftThreshold()
• Build modules with blockwiseModules()

Step 4: Associate modules with traits

• Encode groups as a trait matrix
• Compute module-trait and gene-trait correlations
• Rank modules by absolute correlation with the selected trait

Step 5: Export results

• Save summary tables, module assignments, and per-module gene exports
• Generate dendrogram, heatmap, TOM, and scatter plot PDFs
• Save session info and serialized R objects

---

Error Handling

Common Errors

Error	Cause	Solution
SKILL_FILE_NOT_FOUND	Input file path is wrong or a required TOM file is missing	Check file paths and rerun the analysis from the start
SKILL_MISSING_COLUMNS	Required sample, group, or gene identifier columns are missing or empty	Verify the CSV header and column names
SKILL_EMPTY_DATA	Input is empty, no genes pass filtering, or the filtered matrix is too small for WGCNA	Check data quality and relax filtering settings
SKILL_INVALID_PARAMETER	A CLI value is missing, out of range, or not in the allowed choices	Review parameter values in the command
SKILL_SAMPLE_MISMATCH	Group file sample IDs do not match expression matrix sample IDs	Make sample identifiers identical across both files
SKILL_PACKAGE_NOT_FOUND	A required R package is not installed	Install missing packages before running

IF error persists, READ: references/troubleshooting.md

---

Testing

Smoke Test

# Check CLI help
Rscript scripts/main.R --help

# Run with bundled test data
Rscript scripts/main.R \
  --input_file tests/data/expression.csv \
  --group_file tests/data/group.csv \
  --output_dir tests/output-skill/ \
  --sample_column sample \
  --group_column group \
  --network_type unsigned \
  --cor_type pearson \
  --mad_quantile 0.25 \
  --min_mad 0.01 \
  --max_genes 500 \
  --min_module_size 20 \
  --merge_cut_height 0.25 \
  --soft_r2_cutoff 0.8 \
  --module_of_interest auto \
  --top_modules 1 \
  --tom_sample_size 150 \
  --chunk_size 0 \
  --seed 42 \
  --timeout_seconds 0

# Validate required outputs
Rscript tests/validate_outputs.R tests/output-skill/

Validation Commands

Rscript tests/validate_outputs.R tests/output-skill/

Expected outputs include session_info.txt, the plot PDFs, table CSVs, and the serialized R objects listed above.

Additional Verification

• When exporting explicit modules, confirm the matching tables/module_genes_<module>.csv files exist.
• When generating module membership plots, confirm the matching plots/module_membership_vs_trait_<module>_<trait>.pdf files exist.
• If chunked loading is enabled, compare tables/analysis_summary.csv against a non-chunked run on the same input.

Notes

• Re-running the same command with the same seed is expected to be reproducible.
• Failed runs may still create output_dir and session_info.txt before exiting; this is expected and safe to overwrite on retry.

---

Implementation Checklist

• CLI parsing with optparse
• set.seed() for reproducibility
• Dependency checks with explicit package loading
• Relative source() paths via get_script_dir()
• Optional timeout parameter
• Optional chunked loading for large matrices
• Session info recording
• Error handling with SKILL_* codes
• SKILL.md parameters match the implemented CLI
• File reading instructions in SKILL.md
• Test data provided in tests/data/
• Smoke-test output validation script provided

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Last updated: 2026-04-17 | Version: 1.0.0