cGAP: Generalized Association Plots with HOMALS-Guided Heatmaps for Visualization of High-Dimensional Categorical Data
Visualizing messy categorical data while keeping the original numbers visible
Researchers created cGAP, a visualization tool that makes sense of high-dimensional categorical data—like genetic markers, survey responses, or biological classifications—by embedding the data in a color-coded heatmap that preserves the original matrix while revealing hidden patterns. The approach uses a statistical embedding method called HOMALS to assign colors to similar data points, then reorders rows and columns to surface clusters and outliers that would otherwise remain buried in tables.
Most visualization tools for categorical data either collapse it into simplified charts that lose information or produce abstract plots that disconnect results from the original data—making it hard to trace why a pattern appeared and whether it's real. cGAP solves this by keeping the raw data visible while layering interpretable geometric structure on top, letting researchers across genetics, biomedicine, and social science spot meaningful patterns without sacrificing transparency or accuracy.