Multi-Sample and Multi-Group Spatial Colocalization Analysis Using PANORAMIC

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ID: 323204
2026
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Abstract
MOTIVATION: Spatial omics studies compare cell-cell organization across samples, but most methods model between-sample variability while treating sample-level spatial estimates as error-free. Overlooking within-sample uncertainty can distort inference in heterogeneous cohorts, motivating methods that explicitly quantify and propagate this uncertainty into cohort-level analyses. RESULTS: We present PANORAMIC, a hierarchical framework for spatial colocalization analysis that uses edge-corrected neighborhood enrichment to estimate local cell-type colocalization, spatial bootstrapping to quantify within-sample uncertainty, and multilevel random-effects meta-analysis to propagate this uncertainty across samples, patients, and conditions. In simulations, PANORAMIC improved recovery of within-sample uncertainty and between-sample heterogeneity relative to naive estimators across diverse spatial settings and progressive data degradation. Applied to a colorectal cancer tissue microarray profiled by multiplexed immunofluorescence imaging, PANORAMIC identified stronger B- and T-cell colocalization in tumors with Crohn's-like reaction than in tumors with diffuse inflammatory infiltration, together with tighter higher-order immune organization consistent with immune aggregates. These findings were missed using standard methods, showing that propagating within-sample spatial uncertainty can improve cohort-level inference in spatial omics studies. AVAILABILITY AND IMPLEMENTATION: PANORAMIC is released as an open-source R package at https://github.com/plevritis-lab/panoramic and archived on Zenodo at https://doi.org/10.5281/zenodo.19927197. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7172158694 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Jacob Chang, Almudena Espín Pérez, Perla Molina, Rohit Khurana, Weiruo Zhang, Tian Lu, Sylvia K. Plevritis
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag546
URL
https://doi.org/10.1093/bioinformatics/btag546
Keywords Keywords not found

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