geneSCOPE: gene Spatial Co-Occurrence of Pairwise Expression

Spatial transcriptomics captures neighborhood–dependent gene expression, but existing workflows do not always fully account for measurement scale and often treat space implicitly. We present geneSCOPE, a framework that integrates ecology–inspired statistics with network analysis. Molecules are binned on a grid whose width is chosen near the mode of the per–gene unit–invariant knee (UIK) distribution derived from Morisita′s Iδ—width curves. Pairwise adjacency–weighted spatial association is quantified with Lee′s L. We then assemble a spatial gene network and identify gene modules by consensus clustering. To identify cell—cell interactions between different cell types, high Lee′s L and low Pearson′s r is examined. Applied to human colorectal cancer (three Xenium sections) and a lymph node, geneSCOPE recovered spatial gene modules that map to microanatomy such as invasive margins, luminal epithelium, fibroblast-rich territories and germinal–center subdomains, and highlights intercellular neighborhood patterns at tumor–stroma interfaces between LGR5–marked stem–like tumor programs and C3–centered fibroblast/complement-associated niches. geneSCOPE thus provides a scalable, interpretable analytical foundation that generalizes across spatial omics and supports a wide range of applications.