GPTomics/bio-gene-regulatory-networks-differential-networks

Version Compatibility

Reference examples tested with: DiffCorr 0.4.1+, DINGO/iDINGO 1.0.4+, CoDiNA 1.1.2+; Python path uses scipy 1.12+, statsmodels 0.14+, networkx 3.0+.

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters
• Python: pip show <package> then help(module.function) to check signatures

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

In statsmodels, multipletests() defaults to method 'hs' (Holm-Sidak), NOT Benjamini-Hochberg. Always pass method='fdr_bh' explicitly for differential-correlation FDR.

Differential Networks

"Compare gene co-expression networks between my disease and control groups" -> Test whether gene-gene relationships differ between two conditions, identifying gained, lost, and reversed edges and the genes that rewire.

• R: DiffCorr::comp.2.cc.fdr() (pairwise Fisher z); DiffCoEx (module-level); iDINGO::dingo() (partial-correlation)
• Python: Fisher z-test with scipy.stats + statsmodels FDR

The Single Most Important Modern Insight -- Differential Connectivity Is Not Differential Expression

A gene can have identical mean expression in two conditions yet a completely rewired set of correlation partners -- and that rewiring, not the mean shift, can be the disease signal. The classic demonstration is Hudson, Reverter & Dalrymple 2009 (PLoS Comput Biol 5:e1000382): myostatin received the top Regulatory Impact Factor despite not being differentially expressed, correctly fingering the gene carrying the causal mutation purely from the change in its correlation wiring to differentially-expressed targets. So differential expression (a shift in means) and differential connectivity (a shift in the correlation structure) are orthogonal questions, and the most differentially-connected hub is often not differentially expressed. Three distinct analyses are routinely conflated and must be kept separate: differential expression (mean shift), differential co-expression (pairwise correlation shift, DiffCorr/DiffCoEx), and differential connectivity/rewiring at the conditional-independence level (DINGO).

The dominant practical failure is statistical power. The variance of a difference of two correlations is large, so rewiring detection needs many samples per group -- far more than differential expression. Worse, pairwise differential-correlation testing has a multiple-testing explosion: p genes produce ~p^2/2 edge tests, so without aggressive FDR (or a module-level method that sidesteps per-edge testing) the results are dominated by false positives. Most "rewired hub" findings in small cohorts are underpowered noise.

Differential-Network Method Taxonomy

| Method | Citation | Level | Edge type | Note | |--------|----------|-------|-----------|------| | DiffCorr | Fukushima 2013 Gene | per-edge | marginal | simple Fisher z; p^2/2 tests -> aggressive FDR needed | | DiffCoEx | Tesson 2010 BMC Bioinformatics | module | marginal | WGCNA-based; tests modules, sidesteps per-edge multiplicity | | DINGO | Ha 2015 Bioinformatics | per-edge | partial | group-specific GGM; bootstrap differential score (direct rewiring) | | iDINGO | Class 2018 Bioinformatics | per-edge | partial, multi-omics | chain-graph across data types (e.g. miRNA->mRNA->protein) | | CoDiNA | Gysi 2020 PLoS ONE | per-edge | on supplied nets | compares >=2 networks; common/specific/different edge classes |

Decision Tree by Scenario

| Scenario | Recommended | Why | |----------|-------------|-----| | Two conditions, quick pairwise rewiring | DiffCorr (Fisher z) + strict FDR | simplest; report gained/lost/reversed | | Want modules that rewire, not edges | DiffCoEx | module-level testing avoids the p^2/2 explosion | | Need direct (not indirect) rewiring | DINGO/iDINGO | partial correlation removes confounded indirect changes | | More than two conditions | CoDiNA | n-way comparison with edge classification | | Multi-omics rewiring | iDINGO | chain-graph respects the biological hierarchy | | Just want mean-expression changes | -> differential-expression/de-results | that is DE, not rewiring | | Build the per-condition networks first | -> coexpression-networks | rewiring compares already-built networks |

DiffCorr: Pairwise Differential Correlation (R)

Goal: Find gene pairs whose correlation differs significantly between two conditions.

Approach: Fisher z-transform each correlation per condition and test the z-difference with FDR; classify surviving edges as gained, lost, or reversed.

library(DiffCorr)

expr_all <- read.csv('normalized_counts.csv', row.names = 1)        # genes x samples
info <- read.csv('sample_info.csv', row.names = 1)
# Filter to top variable genes first: p^2/2 edge tests make the full matrix intractable.
gene_vars <- apply(expr_all, 1, var)
top <- names(sort(gene_vars, decreasing = TRUE))[1:3000]
d1 <- expr_all[top, info$condition == 'control']
d2 <- expr_all[top, info$condition == 'disease']

# Returns the differential correlations directly (threshold filters exported pairs by lfdr).
# It only writes the file when save = TRUE, so use the returned data.frame. Columns carry
# spaces ('molecule X', 'molecule Y', 'r1', 'r2', 'lfdr (difference)') -- index with [[ ]].
res <- comp.2.cc.fdr(data1 = d1, data2 = d2, threshold = 0.05, save = TRUE,
                     output.file = 'diffcorr.txt')

DINGO: Direct Differential Rewiring (R)

Goal: Detect rewiring at the conditional-independence (direct edge) level rather than marginal correlation.

Approach: Estimate a group-specific Gaussian graphical model and bootstrap an edge-wise differential score.

library(iDINGO)
# dingo(dat, x, ...): dat = samples x genes; x = the binary group covariate (length n).
fit <- dingo(dat = expr_mat, x = group, B = 100, cores = 8)   # B = bootstrap reps
# fit$diff.score / fit$p.val give edge-wise differential connectivity (direct edges).

Python: Fisher z Differential Network

Goal: Compare correlation networks between two conditions in a Python-native workflow.

Approach: Compute per-condition correlation matrices, test each pair with Fisher's z, apply BH FDR (explicitly), and classify edges.

import numpy as np, pandas as pd
from scipy import stats
from statsmodels.stats.multitest import multipletests

def fisher_z(r1, n1, r2, n2):
    z1, z2 = np.arctanh(np.clip([r1, r2], -0.9999, 0.9999))
    se = np.sqrt(1 / (n1 - 3) + 1 / (n2 - 3))
    z = (z1 - z2) / se
    return z, 2 * stats.norm.sf(abs(z))

def differential_network(e1, e2, fdr=0.05):
    genes = e1.columns.tolist()
    n1, n2 = len(e1), len(e2)
    c1, c2 = e1.corr().values, e2.corr().values
    rows = []
    for i in range(len(genes)):
        for j in range(i + 1, len(genes)):
            z, p = fisher_z(c1[i, j], n1, c2[i, j], n2)
            rows.append((genes[i], genes[j], c1[i, j], c2[i, j], z, p))
    df = pd.DataFrame(rows, columns=['g1', 'g2', 'r1', 'r2', 'z', 'p'])
    # statsmodels default is Holm-Sidak ('hs'); BH must be requested explicitly.
    df['padj'] = multipletests(df['p'], method='fdr_bh')[1]
    return df

Per-Method Failure Modes

Underpowered rewiring claims

Trigger: declaring rewired hubs from a small cohort. Mechanism: the variance of a correlation difference is large; rewiring needs more samples than DE. Symptom: few or no edges survive FDR, or unstable results across resampling. Fix: require adequate n per group; treat low-power results as exploratory.

Pairwise multiple-testing explosion

Trigger: testing all gene pairs with weak/no FDR. Mechanism: p genes -> ~p^2/2 tests. Symptom: thousands of "significant" edges, irreproducible. Fix: pre-filter to variable genes, apply strict FDR, or use a module-level method (DiffCoEx).

Conflating DE with rewiring

Trigger: interpreting differentially-connected genes as differentially expressed (or vice versa). Mechanism: they are orthogonal. Symptom: a rewired hub dismissed because it is not DE. Fix: report DE and differential connectivity separately; a non-DE gene can be the key rewired hub.

Marginal rewiring read as direct

Trigger: interpreting a DiffCorr gained edge as a direct regulatory change. Mechanism: marginal correlation mixes direct and indirect edges; a changed edge may reflect a shifted common driver. Symptom: mechanistic claims from marginal rewiring. Fix: use DINGO (partial correlation) when directness matters.

Holm-Sidak instead of BH

Trigger: multipletests(p) without method=. Mechanism: statsmodels defaults to 'hs', more conservative than intended. Symptom: unexpectedly few hits. Fix: pass method='fdr_bh'.

Quantitative Thresholds

| Threshold | Source | Rationale | |-----------|--------|-----------| | >= 15-20 samples per group | correlation-stability convention | rewiring is lower-powered than DE; small n gives noise | | Pre-filter to top ~2000-5000 variable genes | practical | bounds the p^2/2 test count | | BH FDR < 0.05 | standard | controls the false-discovery rate across many edge tests | | effect-size filter abs(delta r) > 0.3 | convention | avoid reporting trivially different correlations | | DINGO bootstrap B = 100 | iDINGO default-scale | stabilizes the differential score |

Common Errors

| Error / symptom | Cause | Solution | |-----------------|-------|----------| | millions of edge tests / out of memory | full gene matrix | pre-filter to variable genes | | far fewer hits than expected | statsmodels Holm-Sidak default | use method='fdr_bh' | | rewired hub "should be DE" objection | conflating connectivity with expression | report them as separate, orthogonal results | | DGCA not installable from CRAN | archived May 2024 | install from GitHub (andymckenzie/DGCA) | | reversed edges look like noise | no effect-size filter | require abs(delta r) above a threshold |

References

• Hudson NJ, Reverter A, Dalrymple BP. 2009. A differential wiring analysis... correctly identifies the gene containing the causal mutation. PLoS Comput Biol 5(5):e1000382.
• de la Fuente A. 2010. From 'differential expression' to 'differential networking'. Trends Genet 26(7):326-333.
• Fukushima A. 2013. DiffCorr: analyze and visualize differential correlations in biological networks. Gene 518(1):209-214.
• Tesson BM, Breitling R, Jansen RC. 2010. DiffCoEx: differentially coexpressed gene modules. BMC Bioinformatics 11:497.
• Ha MJ, Baladandayuthapani V, Do KA. 2015. DINGO: differential network analysis in genomics. Bioinformatics 31(21):3413-3420.
• Class CA, Ha MJ, Baladandayuthapani V, Do KA. 2018. iDINGO: integrative differential network analysis in genomics. Bioinformatics 34(7):1243-1245.
• Gysi DM, et al. 2020. Co-expression differential network analysis (CoDiNA). PLoS ONE 15(10):e0240523.

Related Skills

• coexpression-networks - build the per-condition co-expression networks being compared
• scenic-regulons - TF regulon activity differences as a complementary rewiring readout
• grn-inference - VIPER differential protein activity between conditions
• differential-expression/de-results - differential expression of means (the orthogonal question)
• temporal-genomics/temporal-grn - time-resolved network change across stages