Document Type
Article
Publication Date
10-15-2025
Published In
PLoS Computational Biology
Abstract
Multiplexed imaging allows multiple cell types to be simultaneously visualised in a single tissue sample, generating unprecedented amounts of spatially-resolved, biological data. In topological data analysis, persistent homology provides multiscale descriptors of “shape" suitable for the analysis of such spatial data. Here we propose a novel visualisation of persistent homology (PH) and fine-tune vectorisations thereof (exploring the effect of different weightings for persistence images, a prominent vectorisation of PH). These approaches offer new biological interpretations and promising avenues for improving the analysis of complex spatial biological data especially in multiple cell type data. To illustrate our methods, we apply them to a lung data set from fatal cases of COVID-19 and a data set from lupus murine spleen.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
M. Torras-Pérez; Iris Yoon , '13; P. Weeratunga; L.-P. Ho; H. M. Byrne; U. Tillmann; and H. A. Harrington.
(2025).
"Topology Across Scales On Heterogeneous Cell Data".
PLoS Computational Biology.
Volume 21,
Issue 10.
DOI: 10.1371/journal.pcbi.1013460
https://works.swarthmore.edu/fac-math-stat/347