دورية أكاديمية
Extended correlation functions for spatial analysis of multiplex imaging data
| العنوان: | Extended correlation functions for spatial analysis of multiplex imaging data |
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| المؤلفون: | Joshua A. Bull, Eoghan J. Mulholland, Simon J. Leedham, Helen M. Byrne |
| المصدر: | Biological Imaging, Vol 4 (2024) |
| بيانات النشر: | Cambridge University Press, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Biology (General) LCC:Medical technology |
| مصطلحات موضوعية: | Digital pathology, image analysis, multiplex imaging, pair correlation function, spatial statistics, Biology (General), QH301-705.5, Medical technology, R855-855.5 |
| الوصف: | Imaging platforms for generating highly multiplexed histological images are being continually developed and improved. Significant improvements have also been made in the accuracy of methods for automated cell segmentation and classification. However, less attention has focused on the quantification and analysis of the resulting point clouds, which describe the spatial coordinates of individual cells. We focus here on a particular spatial statistical method, the cross-pair correlation function (cross-PCF), which can identify positive and negative spatial correlation between cells across a range of length scales. However, limitations of the cross-PCF hinder its widespread application to multiplexed histology. For example, it can only consider relations between pairs of cells, and cells must be classified using discrete categorical labels (rather than labeling continuous labels such as stain intensity). In this paper, we present three extensions to the cross-PCF which address these limitations and permit more detailed analysis of multiplex images: topographical correlation maps can visualize local clustering and exclusion between cells; neighbourhood correlation functions can identify colocalization of two or more cell types; and weighted-PCFs describe spatial correlation between points with continuous (rather than discrete) labels. We apply the extended PCFs to synthetic and biological datasets in order to demonstrate the insight that they can generate. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2633903X 2633-903X |
| Relation: | https://www.cambridge.org/core/product/identifier/S2633903X24000011/type/journal_article; https://doaj.org/toc/2633-903X |
| DOI: | 10.1017/S2633903X24000011 |
| URL الوصول: | https://doaj.org/article/f9de153b25ec4024a816ba01e8a5871e |
| رقم الأكسشن: | edsdoj.f9de153b25ec4024a816ba01e8a5871e |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 2633903X |
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| DOI: | 10.1017/S2633903X24000011 |