ArticleBioinformatics advances2026
Region-aware bridge modeling enables interpretable mesoscale representation of spatial transcriptomic tissue sections.
Article in Bioinformatics advances, 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
Motivation: Spatial transcriptomics maps tissue architecture at high resolution, but spot-level maps are difficult to compare across sections, whereas whole-section averages obscure regional organization. We developed region-aware bridge modeling to aggregate interpretable biological program features into compact mesoscale summaries. Results: Using public colorectal cancer and breast cancer 10x Genomics high-definition Visium sections, we constructed epithelial-like, fibroblast, smooth or myoepithelial, and extracellular matrix bridge features from cell-state indicators, curated gene-program scores, and quality-control summaries. Median-quadrant aggregation produced an eight-region design matrix. Within-section validation showed non-random mesoscale heterogeneity: region-label shuffling reduced the overall mean absolute pairwise regional difference from 0.298 to 0.0020 in colorectal cancer and from 0.328 to 0.0024 in breast cancer, with empirical upper-tail probability values of 0.0002 for both analyses across 5000 permutations. Shifted and rotated partitions changed the overall heterogeneity metric by less than 10%. Exploratory ridge modeling identified fibroblast as the only non-target predictor in the extracellular matrix model, and Bayesian sensitivity analysis supported a positive fibroblast-extracellular matrix association. Supplementary lung, prostate, and ovarian cancer sections supported workflow applicability. Availability and implementation: Code and processed outputs are available through GitHub and Zenodo under 10.5281/zenodo.19801620.
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