ArticleStatistics and data science in imaging2025
Benchmarking Spatial Co-Localization Methods for Single-Cell Multiplex Imaging Data with Applications to High-Grade Serous Ovarian and Triple Negative Breast Cancer.
Article in Statistics and data science in imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- SHADE: A multilevel Bayesian framework for modeling directional spatial interactions in tissue microenvironments.PLoS computational biology · 2026Article
- scSpatialSIM: a simulator of spatial single-cell molecular data.SoftwareX · 2025Article
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10 authors.
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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
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