Pathway Over-Representation Analysis (2D)
Interactive 2D pathway over-representation analysis: watch significant and pathway genes settle into a physics-driven Venn diagram, then read the exact hypergeometric (Fisher's exact test) enrichment p-value live, cross-checked against a Monte Carlo null distribution.
Over-representation analysis is the other standard pathway-analysis method used across genomics, alongside rank-based approaches like GSEA: rather than walking a ranked list, it draws a hard line between "significant" and "not significant" genes and asks whether a pathway's members land on the significant side more often than chance predicts. This simulator builds a background population of genes, lets you choose how strongly pathway membership correlates with significance, and computes the real hypergeometric (Fisher's exact test) enrichment p-value live — visualized as a physics-driven Venn diagram whose circles emerge from real spring and repulsion forces, paired with the exact probability distribution and a Monte Carlo null check.
Watch significant and pathway genes settle into a real physics-driven Venn diagram, then read the exact hypergeometric (Fisher's exact test) enrichment p-value live, cross-checked against a Monte Carlo null distribution — the classic over-representation counterpart to rank-based GSEA.
2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install