Chromatic Topological Data Analysis
Sebastiano Cultrera di Montesano, Ondrej Draganov, Herbert Edelsbrunner, Morteza Saghafian
TL;DR
This survey tackles how to quantify interactions across multiple colored point configurations using topological data analysis. It introduces chromatic $\oldsymbol{\alpha}$-complexes and the chromatic Delaunay mosaic to encode multi-color spatial relations, and packages these insights into the 6-pack of persistent diagrams that jointly describe domain, codomain, kernels, images, and cokernels. A key contribution is the mingling framework, particularly the MST-ratio, which links color mixing to measurable geometric quantities via persistent homology, with rigorous bounds and asymptotics across finite, random, and lattice point sets. The work also develops the algebraic machinery and subcomplex choices needed to extract stable, interpretable descriptors from chromatic data, offering concrete avenues for applications in biology and discrete geometry and pointing to future directions like higher-order colorings, higher dimensions, and stochastic analyses. Overall, the paper provides a comprehensive blueprint for stable, multi-color topological descriptors that quantify complex inter-set interactions at multiple spatial scales, with practical implications for analyzing tumor microenvironments and related discrete-geometric questions.
Abstract
Exploring the shape of point configurations has been a key driver in the evolution of TDA (short for topological data analysis) since its infancy. This survey illustrates the recent efforts to broaden these ideas to model spatial interactions among multiple configurations, each distinguished by a color. It describes advances in this area and prepares the ground for further exploration by mentioning unresolved questions and promising research avenues while focusing on the overlap with discrete geometry.
