ArticleBriefings in bioinformatics2026
geneSCOPE: gene spatial co-occurrence of pairwise expression.
Article in Briefings in bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Spatial transcriptomics captures context-dependent gene expression; however, existing workflows do not consistently account for measurement scale and often rely on a user-defined spatial neighbor graph, making results sensitive to this choice and limiting cross-study comparability. We present geneSCOPE (gene Spatial Co-Occurrence of Pairwise Expression), a framework that integrates ecology-inspired spatial statistics with network analysis to explicitly capture measurement scale and spatial information. In this framework, molecules are binned on a grid with a width selected near the mode of the per-gene unit-invariant knee distribution derived from Morisita's ${I}_{\delta }$-width curves. Pairwise adjacency-weighted spatial associations are quantified using Lee's L. Then, a spatial gene network is assembled, and gene modules are identified via consensus clustering. Cell-cell interactions among various cell types are identified based on high Lee's L with low cell-level co-expression (Pearson's $r$). When applied to transcriptome data derived from human colorectal cancer and lymph node Xenium tissue sections (N = 3 and 1, respectively), geneSCOPE recovered spatial gene modules that mapped to microanatomical compartments such as invasive margins, luminal epithelium, fibroblast-rich territories, and germinal-center subdomains. Further, it highlighted intercellular neighborhood patterns at tumor-stroma interfaces characterized by the co-occurrence of leucine-rich repeat-containing G protein-coupled receptor 5 (LGR5)-marked stem-like tumor programs and complement component 3 (C3)-centered fibroblast/complement-associated niches. Using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database as an external reference for benchmarking, geneSCOPE showed the highest concordance with known interacting gene pairs among the compared methods. In conclusion, geneSCOPE provides a scalable, interpretable, and cross-study comparable framework for gene-centric spatial analysis.
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