Document Type
Article
Publication Date
1-15-2025
Identifier
DOI: 10.1080/29979676.2024.2437947; PMCID: PMC11883755
Abstract
Single-cell multiplex imaging (scMI) measures cell locations and phenotypes within a tissue and can be used to understand the tumor microenvironment. In scMI studies, it is often of interest to quantify spatial co-localization of immune cells and its association with clinical outcomes; however, it remains unknown which of the many available spatial indices have adequate power to detect spatial within-sample co-localization and its association with patient outcomes, such as survival. In this study, the performance of six frequentist metrics of spatial co-localization used in scMI studies were evaluated. Simulated data was used to assess the power and type I error of these spatial metrics to detect signficant co-localization. Furthermore, these spatial co-localization methods were applied to two scMI studies - a high-grade serous ovarian cancer (HGSOC) study and triple negative breast cancer (TNBC) study - to detect within-sample co-localization between cell types and their sensitivity to detect differences in survival across samples. In the simulation study, Ripley's K had the greatest power to identify co-localization followed closely by pair correlation g; all other statistics showed little power across all simulation scenarios. In the application of the methods to cancer studies, the results consistently point to pair correlation g and Ripley's K as indices with the most power for detecting significant co-localization in scMI data. Furthermore, pair correlation g, Ripley's K, and the scLMM index were most effective for estimating between-sample associations between level of co-localization and survival.
Journal Title
Stat Data Sci Imaging
Volume
2
Issue
1
PubMed ID
40051984
Keywords
co-clustering; multiplex imaging; spatial biology; spatial proteomics
Recommended Citation
Soupir AC, Gadiyar IV, Helm BR, et al. Benchmarking Spatial Co-Localization Methods for Single-Cell Multiplex Imaging Data with Applications to High-Grade Serous Ovarian and Triple Negative Breast Cancer. Stat Data Sci Imaging. 2025;2(1):2437947. doi:10.1080/29979676.2024.2437947
Comments
Grants and funding
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher's Link: https://www.tandfonline.com/doi/full/10.1080/29979676.2024.2437947#d1e299