期刊
出版社
NATL ACAD SCIENCES
DOI: 10.1073/pnas.2102166118
关键词
topological data analysis; digital pathology; histology data; tumor immunology hypoxia
资金
- Engineering and Physical Sciences Research Council (EPSRC) [EP/N509711/1]
- Cancer Research UK (CR-UK) Grant through the CR-UK Oxford Centre [C5255/A18085]
- Jean Shanks Foundation/Pathological Society of Great Britain & Ireland Clinical Research Training Fellowship
- Oxfordshire Health Services Research Committee as part of Oxford Hospitals Charity
- National Institute for Health Research Oxford Biomedical Research Centre
- UK Centre for Topological Data Analysis EPSRC [EP/R018472/1]
- EPSRC [EP/R005125/1, EP/T001968/1]
- Royal Society [RGF/EA/201074, UF150238]
- Emerson Collective
- EPSRC [EP/R018472/1, EP/T001968/1, EP/R005125/1] Funding Source: UKRI
This article introduces a mathematical method, multiparameter persistent homology (MPH), for analyzing spatial data of complex systems. The application of MPH landscapes in studying immune cell infiltration and the tumor microenvironment demonstrates its advantages over existing spatial statistics, allowing for better quantification and comparison of features in different cell locations.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据