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高多重化成像数据的空间分析以鉴定组织微环境。

Spatial analysis for highly multiplexed imaging data to identify tissue microenvironments.

机构信息

School of Mathematics and Statistics, The University of Sydney, Sydney, New South Wales, Australia.

Sydney Precision Data Science Centre, The University of Sydney, Sydney, New South Wales, Australia.

出版信息

Cytometry A. 2023 Jul;103(7):593-599. doi: 10.1002/cyto.a.24729. Epub 2023 Mar 23.

Abstract

Highly multiplexed in situ imaging cytometry assays have made it possible to study the spatial organization of numerous cell types simultaneously. We have addressed the challenge of quantifying complex multi-cellular relationships by proposing a statistical method which clusters local indicators of spatial association. Our approach successfully identifies distinct tissue architectures in datasets generated from three state-of-the-art high-parameter assays demonstrating its value in summarizing the information-rich data generated from these technologies.

摘要

高多重化的原位成像细胞检测分析技术使得同时研究多种细胞类型的空间组织成为可能。我们通过提出一种聚类局部空间关联指标的统计方法,解决了量化复杂的多细胞关系的挑战。我们的方法成功地识别了三种最先进的高参数检测分析技术生成的数据集的独特组织架构,这证明了它在总结这些技术生成的丰富信息数据方面的价值。

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