Extended correlation functions for spatial analysis of multiplex imaging data.

Autor:	Bull JA; Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK., Mulholland EJ; Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK., Leedham SJ; Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK.; Translational Gastroenterology Unit, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK.; Oxford NIHR Biomedical Research Centre, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK., Byrne HM; Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK.; Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, UK.
Jazyk:	angličtina
Zdroj:	Biological imaging [Biol Imaging] 2024 Feb 15; Vol. 4, pp. e2. Date of Electronic Publication: 2024 Feb 15 (Print Publication: 2024).
DOI:	10.1017/S2633903X24000011
Abstrakt:	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. Competing Interests: The authors declare no competing interests exist. (© The Author(s) 2024.)
Databáze:	MEDLINE
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