geneSCOPE: gene Spatial Co-Occurrence of Pairwise Expression

Abstract

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 Morisitas I{delta} -width curves. Pairwise adjacency-weighted spatial association is quantified with Lees 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 Lees L and low Pearsons 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.