{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T18:16:29Z","timestamp":1787940989570,"version":"build-2784847793"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Warren Alpert Scholarship in Computational Biology","award":["314868"],"award-info":[{"award-number":["314868"]}]},{"DOI":"10.13039\/100000054","name":"NCI","doi-asserted-by":"publisher","award":["U54CA274511"],"award-info":[{"award-number":["U54CA274511"]}],"id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000092","name":"NLM","doi-asserted-by":"publisher","award":["T15LM007033"],"award-info":[{"award-number":["T15LM007033"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Spatial omics studies compare cell\u2013cell 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We present Pooled ANalysis Of VaRiance-Aware Modeling and Inference of Colocalization (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\u2019s-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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag546","type":"journal-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T11:46:25Z","timestamp":1785498385000},"source":"Crossref","is-referenced-by-count":0,"title":["Multi-sample and multi-group spatial colocalization analysis using 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