bio-gene-regulatory-networks-coexpression-networks

name	bio-gene-regulatory-networks-coexpression-networks
description	Build weighted gene co-expression networks to identify modules of co-regulated genes and relate them to phenotypes using WGCNA and CEMiTool. Detects hub genes and module-trait relationships from bulk or single-cell expression data. Use when finding co-expression modules, identifying hub genes, or relating gene networks to clinical or experimental variables.
tool_type	r
primary_tool	WGCNA

Co-expression Networks

Build weighted gene co-expression networks to identify modules of co-regulated genes and relate them to sample traits.

WGCNA Workflow

Required Libraries

library(WGCNA)
options(stringsAsFactors = FALSE)
allowWGCNAThreads()

Input Preparation

# Expression matrix: genes as columns, samples as rows (WGCNA convention)
expr_data <- read.csv('normalized_counts.csv', row.names = 1)
expr_data <- t(expr_data)  # transpose if genes are rows

# Filter low-variance genes
gene_vars <- apply(expr_data, 2, var)
expr_data <- expr_data[, gene_vars > quantile(gene_vars, 0.25)]

# Check for outlier samples
sample_tree <- hclust(dist(expr_data), method = 'average')
plot(sample_tree, main = 'Sample dendrogram')

Soft-Thresholding Power Selection

powers <- c(1:20)
sft <- pickSoftThreshold(expr_data, powerVector = powers, verbose = 5)

# Plot scale-free topology fit
# R^2 > 0.85 is the default target; acceptable range is 0.8-0.9
# Higher R^2 means better scale-free fit; if no power reaches 0.85,
# use the power where R^2 plateaus (but should be >= 0.80)
par(mfrow = c(1, 2))
plot(sft$fitIndices[, 1], -sign(sft$fitIndices[, 3]) * sft$fitIndices[, 2],
     xlab = 'Soft Threshold (power)', ylab = 
     main  
ablineh   col  

plotsftfitIndices  sftfitIndices 
     xlab   ylab  
     main  

soft_power  sftpowerEstimate

Network Construction and Module Detection

net <- blockwiseModules(
    expr_data, power = soft_power,
    TOMType = 'unsigned', minModuleSize = 30,
    reassignThreshold = 0, mergeCutHeight = 0.25,
    numericLabels = TRUE, pamRespectsDendro = FALSE,
    saveTOMs = TRUE, saveTOMFileBase = 'TOM',
    verbose = 3
)

module_colors <- labels2colors(net$colors)
table(module_colors)

plotDendroAndColors(net$dendrograms[[1]], module_colors[net$blockGenes[[1]]],
                    'Module colors', dendroLabels   hang  
                    addGuide   guideHang  

Module Eigengenes and Trait Relationships

# Module eigengenes (first PC of each module)
MEs <- net$MEs
MEs <- orderMEs(MEs)

# Trait data (samples as rows, traits as columns)
traits <- read.csv('sample_traits.csv', row.names = 1)

# Correlate eigengenes with traits
module_trait_cor <- cor(MEs, traits, use = 'p')
module_trait_pval <- corPvalueStudent(module_trait_cor, nrow(expr_data))

# Heatmap of module-trait correlations
textMatrix <- paste(signif(module_trait_cor, 2), '\n(',
                    signif(module_trait_pval, 1), ')', sep = '')
dim(textMatrix) <- module_trait_cor

labeledHeatmapMatrix  module_trait_cor
               xLabels  colnamestraits yLabels  MEs
               ySymbols  MEs colorLabels  
               colors  blueWhiteRed textMatrix  textMatrix
               setStdMargins   cex.text  
               main  

Hub Gene Identification

# Gene module membership (correlation with module eigengene)
module_of_interest <- 'turquoise'
module_genes <- colnames(expr_data)[module_colors == module_of_interest]

gene_module_membership <- cor(expr_data, MEs, use = 'p')
gene_trait_significance <- cor(expr_data, traits$phenotype, use = 'p')

# Hub genes: high module membership AND high trait significance
hub_threshold_mm <- 0.8   # module membership cutoff
hub_threshold_gs <- 0.2   # gene significance cutoff
hub_genes <- module_genes[
    abs(gene_module_membership[module_genes, paste0('ME', module_of_interest)]) > hub_threshold_mm &
    abs(gene_trait_significance[module_genes, 1]) > hub_threshold_gs



connectivity  intramodularConnectivity
    adjacencyexpr_data power  soft_power
    module_colors

top_hubs  connectivitymodule_genes  
    dplyrarrangedesckWithin 
    head

Export for Cytoscape

# Export top connections for network visualization
TOM <- TOMsimilarityFromExpr(expr_data, power = soft_power)
dimnames(TOM) <- list(colnames(expr_data), colnames(expr_data))

module_genes <- colnames(expr_data)[module_colors == module_of_interest]
module_TOM <- TOM[module_genes, module_genes]

cyt <- exportNetworkToCytoscape(module_TOM, edgeFile = 'edges.txt',
                                 nodeFile = 'nodes.txt',
                                 weighted = TRUE, threshold = 0.02)

CEMiTool (Automated Analysis)

library(CEMiTool)

# CEMiTool automates soft-threshold selection and module detection
expr_matrix <- read.csv('normalized_counts.csv', row.names = 1)
sample_annot <- read.csv('sample_annotation.csv')

cem <- cemitool(expr_matrix, sample_annot, filter = TRUE, plot = TRUE, verbose = TRUE)

# Results
nmodules(cem)
module_genes <- module_genes(cem)
generate_report(cem, directory = 'cemitool_report')

# Enrichment with gene sets (optional)
gene_sets <- read_gmt('pathways.gmt')
cem <- mod_ora(cem, gene_sets)
cem <- plot_ora(cem)

hdWGCNA (Single-Cell)

library(hdWGCNA)
library(Seurat)

seurat_obj <- readRDS('clustered.rds')
seurat_obj <- SetupForWGCNA(seurat_obj, gene_select = 'fraction',
                             fraction = 0.05, wgcna_name = 'hdwgcna')

# Metacells to reduce sparsity (groups of 25-50 cells)
seurat_obj <- MetacellsByGroups(seurat_obj, group.by = c('seurat_clusters'),
                                 k = 25, max_shared = 10, ident.group = 'seurat_clusters')
seurat_obj <- NormalizeMetacells(seurat_obj)

# Standard WGCNA on metacells
seurat_obj <- SetDatExpr(seurat_obj, group.by = 'seurat_clusters', group_name = 'all')
seurat_obj  TestSoftPowersseurat_obj
seurat_obj  ConstructNetworkseurat_obj soft_power   setDatExpr  
seurat_obj  ModuleEigengenesseurat_obj
seurat_obj  ModuleConnectivityseurat_obj


seurat_obj  RunModuleUMAPseurat_obj n_hubs   n_neighbors   min_dist  
ModuleFeaturePlotseurat_obj features  

PyWGCNA (Python Alternative)

import PyWGCNA

pywgcna = PyWGCNA.WGCNA(name='analysis', species='homo sapiens',
                         geneExp='normalized_counts.csv', outputPath='pywgcna_output/')
pywgcna.preprocess()
pywgcna.findModules()
pywgcna.updateSampleInfo(path='sample_traits.csv')
pywgcna.analyseWGCNA()

Statistical Considerations

Consideration	Guideline	Rationale
Minimum samples	20 recommended, 15 absolute floor	Correlation stability; below 15, module detection is unreliable
Scale-free R^2	> 0.85 default (0.80-0.90 acceptable)	Ensures biological network topology
Min module size	30 genes	Smaller modules are often noise
Merge cut height	0.25	Merges modules with >75% eigengene correlation
Gene filtering	Top 5000-10000 most variable	Reduces noise, speeds computation

Related Skills

• scenic-regulons - TF-centric regulon inference from scRNA-seq
• differential-networks - Compare networks between conditions
• differential-expression/deseq2-basics - DE analysis to prioritize network genes
• differential-expression/batch-correction - Remove batch effects before network construction
• temporal-genomics/temporal-grn - Dynamic GRN inference from bulk time-series data