Topology across scales on heterogeneous cell data
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.
Authors
- Praveen Weeratunga (ORCID: https://orcid.org/0000-0003-2306-0885)
- Iris H. R. Yoon (ORCID: https://orcid.org/0000-0003-0638-9195)
- Heather A. Harrington (ORCID: https://orcid.org/0000-0002-1705-7869)
- Ulrike Tillmann (ORCID: https://orcid.org/0000-0002-8076-7660)
- Ling‐Pei Ho (ORCID: https://orcid.org/0000-0001-8319-301X)
- Helen M. Byrne (ORCID: https://orcid.org/0000-0003-1771-5910)
- Maria Torras-Pérez
Institutions
- Swarthmore College (US)
- Isaac Newton Institute for Mathematical Sciences (GB)
- University of Cambridge (GB)
- University of Oxford (GB)
- Ludwig Cancer Research (GB)
- MRC Weatherall Institute of Molecular Medicine (GB)
- MRC Human Immunology Unit (GB)
- Center for Systems Biology Dresden (DE)
- Max Planck Institute of Molecular Cell Biology and Genetics (DE)
- Technische Universität Dresden (DE)
- University of Colombo (LK)
Publication Details
- Journal
- PLoS Computational Biology
- Published
- 2025-10-15
- DOI
- https://doi.org/10.1371/journal.pcbi.1013460
- Primary Topic
- Topological and Geometric Data Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00