Iyengar Sourish S, Qin Alex R, Robertson Nicholas, Harman Andrew N, Patrick Ellis
Centre for Cancer Research, Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW 2145, Australia.
Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW 2006, Australia.
Bioinformatics. 2025 Aug 2;41(8). doi: 10.1093/bioinformatics/btaf409.
As research advances in spatially resolving the biological archetype of various diseases, technologies that capture the spatial relationships between cells are demonstrating increasing value. Whilst there are an increasing number of analytical methods being developed to identify the complex web of interactions between cells, the downstream impacts of these cell-cell relationships are under explored.
We present SpatioMark, a statistical framework that simplifies the assessment of gene or protein expression changes within a cell type that are associated with the spatial proximity to other cell types. We demonstrate its performance across spatial proteomics and transcriptomics datasets. We link identified relationships with differences in patient survival. We highlight key challenges in identifying changes in molecular markers associated with the localization of cells. We propose correction strategies that reduce artefact-induced relationships.
SpatioMark is implemented in the Statial R package on Bioconductor: https://bioconductor.org/packages/release/bioc/html/Statial.html.
随着在空间上解析各种疾病生物学原型的研究不断推进，能够捕捉细胞间空间关系的技术正展现出越来越大的价值。虽然目前正在开发越来越多的分析方法来识别细胞间复杂的相互作用网络，但这些细胞 - 细胞关系的下游影响仍有待探索。
我们提出了SpatioMark，这是一个统计框架，可简化对与其他细胞类型空间邻近性相关的细胞类型内基因或蛋白质表达变化的评估。我们展示了它在空间蛋白质组学和转录组学数据集上的性能。我们将所识别的关系与患者生存差异联系起来。我们强调了识别与细胞定位相关的分子标记变化时的关键挑战。我们提出了减少伪像诱导关系的校正策略。
SpatioMark在Bioconductor上的Statial R包中实现：https://bioconductor.org/packages/release/bioc/html/Statial.html 。
