Gene Network Inference Lab (2D)

A hidden ground-truth gene regulatory network of ten genes drives a simulated Hill-function expression time series, exactly like real RNA-seq or scRNA-seq data — but you never see the wiring, only the noisy readings. This 2D lab reconstructs that network with three real inference strategies used in genomics — correlation thresholding, partial-correlation regression, and CRISPR-style single-gene perturbation testing — then scores every inferred edge against the true network across three linked panels: a pannable/zoomable node-link graph, a live inferred-edge matrix heatmap, and the raw noisy expression time series feeding both. Tuning measurement noise, time-series length, and a toggleable hidden confounder shows exactly why perturbation data is considered the gold standard for validating GRNs, and why correlation alone is not: precision, recall and F1 update live as you switch methods.