PT  - JOURNAL ARTICLE
AU  - Iyengar, Sourish S
AU  - Qin, Alex R
AU  - Robertson, Nicholas
AU  - Harman, Andrew N
AU  - Patrick, Ellis
TI  - SpatioMark: Quantifying the impact of spatial proximity on cell phenotype
AID  - 10.1101/2024.12.04.626887
DP  - 2024 Jan 01
TA  - bioRxiv
PG  - 2024.12.04.626887
4099  - http://biorxiv.org/content/early/2024/12/08/2024.12.04.626887.short
4100  - http://biorxiv.org/content/early/2024/12/08/2024.12.04.626887.full
AB  - 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. Here, we present SpatioMark, a statistical framework that simplifies the assessment of gene or protein expression in relation to the spatial proximity of different cell types. We demonstrate its performance across spatial proteomics and transcriptomics datasets and link identified relationships with differences in patient survival. We highlight key challenges in identifying changes in molecular markers associated with the localisation of cells and propose corrections which reduce artefact induced relationships. SpatioMark is implemented in the Statial R package hosted on the Bioconductor Project, ensuring interoperability with existing spatial analysis tools. Ultimately, this work highlights strategies for identifying and interpreting changes in cell phenotype associated with cellular relationships in spatial omics data, with broad applicability across various multiplexed imaging platforms.Competing Interest StatementThe authors have declared no competing interest.