RT Journal Article
SR Electronic
T1 SpatioMark: Quantifying the impact of spatial proximity on cell phenotype
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2024.12.04.626887
DO 10.1101/2024.12.04.626887
A1 Iyengar, Sourish S
A1 Qin, Alex R
A1 Robertson, Nicholas
A1 Harman, Andrew N
A1 Patrick, Ellis
YR 2024
UL http://biorxiv.org/content/early/2024/12/08/2024.12.04.626887.abstract
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.