# SpatioMark: quantifying the impact of spatial proximity on cell phenotype

## TL;DR

SpatioMark is a new statistical tool that helps researchers understand how the spatial arrangement of cells affects gene or protein expression and patient outcomes.

## Contribution

SpatioMark introduces a novel framework for quantifying the impact of spatial proximity on cell phenotype using spatial proteomics and transcriptomics data.

## Key findings

- SpatioMark links spatial cell relationships to gene/protein expression changes and patient survival differences.
- The framework identifies challenges in detecting molecular marker changes due to cell localization.
- Correction strategies are proposed to reduce artefact-induced relationships in spatial data.

## Abstract

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.