ArticleProceedings of the National Academy of Sciences of the United States of America2021
Multiparameter persistent homology landscapes identify immune cell spatial patterns in tumors.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed, 88 citations in OpenAlex.
- MuSpAn: a toolbox for multiscale spatial analysis.Nature communications · 2026Article
- Slide-free FIBI histology for improved quantitative vascular network analysis in breast cancer: Integrating vessel geometry and topology metrics.Journal of pathology informatics · 2026Article
- Topology-Based Biomarkers Accurately Predict Breast Cancer Outcome and Survival.Cancer research · 2026Article
- SpaceBF: spatial coexpression analysis using Bayesian fused approaches in spatial omics datasets.GigaScience · 2026Article
- Detecting clinically relevant topological structures in multiplexed spatial proteomics using TopKAT.Patterns (New York, N.Y.) · 2026Article
- A Review of Topological Data Analysis and Topological Deep Learning in Molecular Sciences.Journal of chemical information and modeling · 2025Review
- Probing omics data via harmonic persistent homology.Scientific reports · 2025Article
- Topology across scales on heterogeneous cell data.PLoS computational biology · 2025Article
- Spatial omics enters the microscopic realm: opportunities and challenges.Trends in genetics : TIG · 2025Review
- The topological properties of the protein universe.Nature communications · 2025Article
- Topological classification of tumour-immune interactions and dynamics.Journal of mathematical biology · 2025Article
- Topological data analysis of pattern formation of human induced pluripotent stem cell colonies.Scientific reports · 2025Article
- Finding and Profiling Renal Cells with Spatial Transcriptomics.Journal of the American Society of Nephrology : JASN · 2025Article
- Topological data analysis in single cell biology.Frontiers in immunology · 2025Review
- Relational Persistent Homology for Multispecies Data with Application to the Tumor Microenvironment.Bulletin of mathematical biology · 2024Article
- Dissecting glial scar formation by spatial point pattern and topological data analysis.Scientific reports · 2024Article
- Article
- SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA.Journal of proteome research · 2024Article
- Morphological multiparameter filtration and persistent homology in mitochondrial image analysis.PloS one · 2024Article
- Extended correlation functions for spatial analysis of multiplex imaging data.Biological imaging · 2024Article
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7 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Highly resolved spatial data of complex systems encode rich and nonlinear information. Quantification of heterogeneous and noisy data-often with outliers, artifacts, and mislabeled points-such as those from tissues, remains a challenge. The mathematical field that extracts information from the shape of data, topological data analysis (TDA), has expanded its capability for analyzing real-world datasets in recent years by extending theory, statistics, and computation. An extension to the standard theory to handle heterogeneous data is multiparameter persistent homology (MPH). Here we provide an application of MPH landscapes, a statistical tool with theoretical underpinnings. MPH landscapes, computed for (noisy) data from agent-based model simulations of immune cells infiltrating into a spheroid, are shown to surpass existing spatial statistics and one-parameter persistent homology. We then apply MPH landscapes to study immune cell location in digital histology images from head and neck cancer. We quantify intratumoral immune cells and find that infiltrating regulatory T cells have more prominent voids in their spatial patterns than macrophages. Finally, we consider how TDA can integrate and interrogate data of different types and scales, e.g., immune cell locations and regions with differing levels of oxygenation. This work highlights the power of MPH landscapes for quantifying, characterizing, and comparing features within the tumor microenvironment in synthetic and real datasets.
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