A Robust Statistical Approach for Finding Informative Spatially Associated Pathways

Abstract

Spatial transcriptomics offers insights into functional localization of cells by mapping gene expression to spatial locations. Traditional focus on selecting spatially variable genes often misses the complexity of biological pathways and biological network dynamics. We introduce a novel framework that shifts the focus towards identifying functional pathways associated with spatial variability, by adapting the Brownian distance covariance test to explore the heterogeneity of biological functions over space. The statistical approach is free of parameter selection. It allows for a deeper understanding of how cells coordinate their activities across different spatial domains through biological processes. By analyzing real human and mouse datasets, the method found significant pathways that were associated with spatial variation, as well as different pathway patterns among inner- and edge-cancer regions. This innovative framework offers a new perspective on analyzing spatial transcriptomic data, contributing to our understanding of tissue architecture and disease pathology.