Home▸Molecular Biology▸Gene Co-Expression Network: Soft-Thresholding for Scale-Free Topology

Gene Co-Expression Network: Soft-Thresholding for Scale-Free Topology

Build a weighted gene co-expression network from simulated expression profiles and tune the WGCNA soft-thresholding power to watch the network's degree distribution collapse toward a scale-free topology, with a live power-law fit R² readout.

Molecular Biology3DAdvanced60 FPS📱 Mobile-adapted

Details

CategoryMolecular Biology

RenderingThree.js · WebGL

DifficultyAdvanced

Target FPS60

ControlsSliders set the soft-thresholding power beta, the module coherence of the underlying simulated expression data, and the edge display/module-detection threshold; toggle buttons switch between unsigned and signed adjacency, and a resample button regenerates the underlying expression dataset.

network biologygene expressionWGCNAscale-free networksystems biologybioinformatics

Related simulations

Protein Interaction Network Robustness — 2D PercolationA 2D companion to the scale-free protein-interaction network sim: an independent 2D…

Protein Interaction Network RobustnessGrow a scale-free protein-protein interaction network and knock out proteins one at a time —…

Multi-Omics Patient Subtyping — 2D Joint NMFA 2D multi-panel view of joint non-negative matrix factorization fusing synthetic genomics,… ⊞ All 602 in Molecular Biology →

Real gene expression data is noisy: a raw correlation matrix between thousands of genes is nearly a fully-connected "hairball", with no usable structure. WGCNA (Weighted Gene Co-Expression Network Analysis) fixes this with a single trick — raise every correlation to a power β before treating it as an edge weight. This simulator builds a small synthetic expression dataset with four hidden co-expression modules, computes the real Pearson correlation network between genes, and lets you drag that soft-thresholding power live: low β keeps everything densely connected, higher β prunes weak edges exponentially faster than strong ones until a handful of hub genes emerge and a live log-log power-law fit (R²) confirms the network has become scale-free — the same diagnostic real WGCNA pipelines use to choose β on actual RNA-seq data.

⚙ Under the hood

Build a weighted gene co-expression network from simulated expression profiles and tune the WGCNA soft-thresholding power live to watch the degree distribution collapse toward a scale-free topology, with a live power-law fit R² readout.

network biologygene expressionWGCNAscale-free networksystems biologybioinformatics

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install