bio-gene-regulatory-networks-differential-networks

name	bio-gene-regulatory-networks-differential-networks
description	Compare gene regulatory and co-expression networks between biological conditions to identify rewired regulatory relationships using DiffCorr. Detects gained, lost, and reversed gene-gene correlations between conditions. Use when comparing co-expression networks between disease vs control, treatment conditions, or developmental stages.
tool_type	r
primary_tool	DiffCorr

Differential Networks

Compare co-expression and regulatory networks between biological conditions to identify rewired gene-gene relationships.

DiffCorr Workflow

DiffCorr uses Fisher's z-transform to test whether the correlation between two genes differs significantly between two conditions.

Required Libraries

library(DiffCorr)
library(igraph)

Input Preparation

# Separate expression matrices by condition (genes as columns, samples as rows)
expr_all <- read.csv('normalized_counts.csv', row.names = 1)
sample_info <- read.csv('sample_info.csv', row.names = 1)

expr_condition1 <- t(expr_all[, sample_info$condition == 'control'])
expr_condition2 <- t(expr_all[, sample_info$condition == 'disease'])

# Filter to variable genes (top 2000-5000 most variable)
gene_vars <- apply(expr_all, 1, var)
top_genes <- names(sort(gene_vars, decreasing = TRUE))[1:3000]
expr_condition1 <- expr_condition1[, top_genes]
expr_condition2 <- expr_condition2[, top_genes]

Run DiffCorr Analysis

# Compute correlation matrices
cor1 <- cor(expr_condition1, method = 'pearson')
cor2 <- cor(expr_condition2, method = 'pearson')

n1 <- nrow(expr_condition1)
n2 <- nrow(expr_condition2)

# Fisher z-transform test for differential correlation
# Compares correlation coefficients between two groups
result <- comp.2.cc.fdr(
    output.file = 'diffcorr_results.txt',
    data1 = expr_condition1,
    data2 = expr_condition2,
    threshold = 0.05  # FDR threshold
)

Parse and Classify Results

diffcorr <- read.delim('diffcorr_results.txt')

# Classify differential correlations
classify_edge <- function(cor1, cor2, threshold = 0.3) {
    if (abs(cor1) < threshold & abs(cor2) >= threshold) return('gained')
    if (abs(cor1) >= threshold & abs(cor2) < threshold) return('lost')
    if (cor1 > threshold & cor2 < -threshold) return('reversed')
     cor1  threshold  cor2  threshold 
    


diffcorredge_type  mapplyclassify_edge diffcorrcor1 diffcorrcor2
tablediffcorredge_type


significant  diffcorrdiffcorrp.adj   
rewired  significantsignificantedge_type   
printpaste nrowrewired

Identify Rewired Hub Genes

# Genes involved in most differential correlations
gene_rewiring <- c(rewired$gene1, rewired$gene2)
rewiring_counts <- sort(table(gene_rewiring), decreasing = TRUE)
top_rewired_genes <- head(rewiring_counts, 20)
print(top_rewired_genes)

# These genes have the most condition-specific regulatory changes

DGCA (Alternative)

DGCA (Differential Gene Correlation Analysis) was archived from CRAN in May 2024. It is still available via GitHub.

# Install from GitHub (archived from CRAN 2024-05)
devtools::install_github('andymckenzie/DGCA')
library(DGCA)

# Run differential correlation analysis
dgca_results <- ddcorAll(
    inputMat = expr_all,
    design = sample_info$condition,
    compare = c('control', 'disease'),
    adjust = 'BH',         # multiple testing correction
    nPerm = 0,             # 0 for analytical p-values (faster)
    corrType = 'pearson'
)

# Filter significant results
sig_dgca <- dgca_results[dgca_results$pValDiff_adj < 0.05, ]

# Classify by correlation change category
# DGCA provides: +/+, +/-, -/+, -/-, +/0, 0/+, -/0, 0/-, 0/0
table(sig_dgca$Classes)

Python NetworkX Approach

import pandas as pd
import numpy as np
from scipy import stats
import networkx as nx

def build_correlation_network(expr_matrix, threshold=0.6):
    '''Build network from correlation matrix, keeping edges above threshold.'''
    cor_matrix = expr_matrix.corr(method='pearson')
    genes = cor_matrix.columns.tolist()
    G = nx.Graph()
    G.add_nodes_from(genes)

    for i in range(len(genes)):
        for j in range(i + 1, len(genes)):
            r = cor_matrix.iloc[i, j]
            if abs(r) >= threshold:
                G.add_edge(genes[i], genes[j], weight=r)
    return G


def fisher_z_test(r1, n1, r2, n2):
    '''Fisher z-transform test for difference between two correlations.'''
    z1 = np.arctanh(np.clip(r1, -0.9999, 0.9999))
    z2 = np.arctanh(np.clip(r2, -0.9999, 0.9999))
    se = np.sqrt(1 / (n1 - 3) + 1 / (n2 - 3))
    z_stat = (z1 - z2) / se
    p_value = 2 * stats.norm.sf(abs(z_stat))
    return z_stat, p_value


def differential_network_analysis(expr_cond1, expr_cond2, fdr_threshold=0.05):
    
    genes = expr_cond1.columns.tolist()
    n1, n2 = (expr_cond1), (expr_cond2)
    cor1 = expr_cond1.corr()
    cor2 = expr_cond2.corr()

    results = []
     i  ((genes)):
         j  (i + , (genes)):
            r1, r2 = cor1.iloc[i, j], cor2.iloc[i, j]
            z_stat, p_val = fisher_z_test(r1, n1, r2, n2)
            results.append({
                : genes[i], : genes[j],
                : r1, : r2,
                : z_stat, : p_val
            })

    df = pd.DataFrame(results)

    
     statsmodels.stats.multitest  multipletests
    _, df[], _, _ = multipletests(df[], method=)

    
     ():
        r1, r2 = row[], row[]
         (r1) < threshold  (r2) >= threshold:
             
         (r1) >= threshold  (r2) < threshold:
             
         r1 > threshold  r2 < -threshold:
             
         r1 < -threshold  r2 > threshold:
             
         

    df[] = df.apply(classify, axis=)
     df



expr_all = pd.read_csv(, index_col=).T
sample_info = pd.read_csv(, index_col=)

expr_ctrl = expr_all.loc[sample_info[sample_info[] == ].index]
expr_disease = expr_all.loc[sample_info[sample_info[] == ].index]


gene_vars = expr_all.var()
top_genes = gene_vars.nlargest().index.tolist()
expr_ctrl, expr_disease = expr_ctrl[top_genes], expr_disease[top_genes]

diff_results = differential_network_analysis(expr_ctrl, expr_disease)
significant = diff_results[diff_results[] < ]
(significant[].value_counts())

Visualize Differential Network

import matplotlib.pyplot as plt

rewired = significant[significant['edge_type'] != 'unchanged']

G_diff = nx.Graph()
color_map = {'gained': '#2ca02c', 'lost': '#d62728', 'reversed': '#9467bd'}

for _, row in rewired.iterrows():
    G_diff.add_edge(row['gene1'], row['gene2'],
                    color=color_map[row['edge_type']], weight=abs(row['z_stat']))

edge_colors = [G_diff[u][v]['color'] for u, v in G_diff.edges()]

fig, ax = plt.subplots(figsize=(12, 12))
pos = nx.spring_layout(G_diff, k=2, seed=42)
nx.draw_networkx(G_diff, pos, edge_color=edge_colors, node_size=50,
                 font_size=6, width=0.5, alpha=0.7, ax=ax)

# Legend
import matplotlib.patches as mpatches
legend_handles = [mpatches.Patch(color=c, label=l) for l, c in color_map.items()]
ax.legend(handles=legend_handles, loc='upper left')
ax.set_title('Differential co-expression network')
plt.savefig('differential_network.pdf', bbox_inches='tight')

Statistical Considerations

Consideration	Guideline	Rationale
Minimum samples per group	20 recommended, 15 floor	Correlation estimates unstable below 15
Multiple testing	BH FDR correction	Thousands of gene pairs tested
Correlation method	Pearson (default)	Spearman for non-linear; Pearson is standard
Gene filtering	Top 2000-5000 variable	Reduces test burden, focuses on informative genes
Effect size filter	abs(delta_r) > 0.3	Avoid reporting trivially different correlations

Related Skills

• coexpression-networks - Build WGCNA networks for individual conditions
• scenic-regulons - TF regulon inference from scRNA-seq
• differential-expression/deseq2-basics - DE analysis to complement network rewiring
• temporal-genomics/temporal-grn - Time-delayed regulatory inference from temporal data