{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T09:06:43Z","timestamp":1779095203041,"version":"3.51.4"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"2","license":[{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["5T32CA134286-14"],"award-info":[{"award-number":["5T32CA134286-14"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01CA293883"],"award-info":[{"award-number":["U01CA293883"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01CA263494"],"award-info":[{"award-number":["R01CA263494"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P30CA016672"],"award-info":[{"award-number":["P30CA016672"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"U.S. National Science Foundation","doi-asserted-by":"publisher","award":["DMS-2547531"],"award-info":[{"award-number":["DMS-2547531"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000054","name":"National Cancer Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000054","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Oral squamous cell carcinomas (OSCC), the predominant head and neck cancer, pose significant challenges due to late-stage diagnoses and low five-year survival rates. Spatial transcriptomics offers a promising avenue to decipher the genetic intricacies of OSCC tumor microenvironments. In spatial transcriptomics, Cell-type deconvolution is a crucial inferential goal; however, current methods fail to consider the high zero-inflation present in OSCC data. To address this, we develop a novel zero-inflated version of the hierarchical generalized transformation model (ZI-HGT) and apply it to the Conditional AutoRegressive Deconvolution (CARD) for cell-type deconvolution. The ZI-HGT serves as an auxiliary Bayesian technique for CARD, reconciling the highly zero-inflated OSCC spatial transcriptomics data with CARD\u2019s normality assumption. The combined ZI-HGT + CARD framework achieves enhanced cell-type deconvolution accuracy and quantifies uncertainty in the estimated cell-type proportions. We demonstrate the superior performance through simulations and analysis of the OSCC data. Furthermore, our approach enables the determination of the locations of the diverse fibroblast population in the tumor microenvironment, critical for understanding tumor growth and immunosuppression in OSCC.<\/jats:p>","DOI":"10.1093\/biomtc\/ujag055","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T12:41:39Z","timestamp":1773924099000},"source":"Crossref","is-referenced-by-count":0,"title":["A zero-inflated hierarchical generalized transformation model to address non-normality in spatially-informed cell-type deconvolution"],"prefix":"10.1093","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8692-7178","authenticated-orcid":false,"given":"Hunter J","family":"Melton","sequence":"first","affiliation":[{"name":"Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth , Hanover, NH 03756 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7000-2576","authenticated-orcid":false,"given":"Jonathan R","family":"Bradley","sequence":"additional","affiliation":[{"name":"Department of Statistics and Data Science, The University of Missouri , Columbia, MO 65201 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8400-1785","authenticated-orcid":false,"given":"Chong","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, University of Texas MD Anderson Cancer Center , Houston, TX 77030 ,","place":["United States"]},{"name":"Institute for Data Science in Oncology, University of Texas MD Anderson Cancer Center , Houston, TX 77030 ,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,4,17]]},"reference":[{"key":"2026051804121345000_bib1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-023-40271-4","article-title":"Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response","volume":"14","author":"Arora","year":"2023","journal-title":"Nature Communications"},{"key":"2026051804121345000_bib2","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1111\/j.2517-6161.1974.tb00999.x","article-title":"Spatial interaction and the statistical analysis of lattice systems","volume":"36","author":"Besag","year":"1974","journal-title":"Journal of the Royal Statistical Society Series B"},{"key":"2026051804121345000_bib3","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1111\/j.2517-6161.1964.tb00553.x","article-title":"An analysis of transformations","volume":"26","author":"Box","year":"1964","journal-title":"Journal of the Royal Statistical Society Series B"},{"key":"2026051804121345000_bib4","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1214\/20-BA1246","article-title":"Joint Bayesian analysis of multiple response-types using the hierarchical generalized transformation model","volume":"17","author":"Bradley","year":"2022","journal-title":"Bayesian Analysis"},{"key":"2026051804121345000_bib5","first-page":"81","article-title":"Hierarchical models for spatial data with errors that are correlated with the latent process","volume":"30","author":"Bradley","year":"2020","journal-title":"Statistica Sinica"},{"key":"2026051804121345000_bib6","doi-asserted-by":"crossref","first-page":"100749","DOI":"10.1016\/j.spasta.2023.100749","article-title":"Deep hierarchical generalized transformation models for spatio-temporal data with discrepancy errors","volume":"55","author":"Bradley","year":"2023","journal-title":"Spatial Statistics"},{"key":"2026051804121345000_bib7","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1038\/s41587-021-00830-w","article-title":"Robust decomposition of cell type mixtures in spatial transcriptomics","volume":"40","author":"Cable","year":"2022","journal-title":"Nature Biotechnology"},{"key":"2026051804121345000_bib8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-022-28020-5","article-title":"Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data","volume":"13","author":"Danaher","year":"2022","journal-title":"Nature Communications"},{"key":"2026051804121345000_bib9","doi-asserted-by":"crossref","first-page":"e50","DOI":"10.1093\/nar\/gkab043","article-title":"SPOTlight: seeded NMF regression to deconvolute spatial transcriptomics spots with single-cell transcriptomes","volume":"49","author":"Elosua-Bayes","year":"2021","journal-title":"Nucleic Acids Research"},{"key":"2026051804121345000_bib10","article-title":"Accounting for excess zeros and sample selection in Poisson and negative binomial regression models","author":"Greene","year":"1994","journal-title":"NYU Working Paper No. EC-94-10"},{"key":"2026051804121345000_bib11","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1038\/s41416-019-0409-6","article-title":"Tissue-infiltrating immune cells as prognostic markers in oral squamous cell carcinoma: a systematic review and meta-analysis","volume":"120","author":"Hadler-Olsen","year":"2019","journal-title":"British Journal of Cancer"},{"key":"2026051804121345000_bib12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2307\/1269547","article-title":"Zero-inflated Poisson regression, with an application to defects in manufacturing","volume":"34","author":"Lambert","year":"1992","journal-title":"Technometrics"},{"key":"2026051804121345000_bib13","first-page":"1","article-title":"A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics","volume":"14","author":"Li","year":"2023","journal-title":"Nature Communications"},{"key":"2026051804121345000_bib14","doi-asserted-by":"crossref","first-page":"4129","DOI":"10.1093\/bioinformatics\/btab455","article-title":"Bayesian modeling of spatial molecular profiling data via Gaussian process","volume":"37","author":"Li","year":"2021","journal-title":"Bioinformatics"},{"key":"2026051804121345000_bib15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-021-27729-z","article-title":"Zero-preserving imputation of single-cell RNA-seq data","volume":"13","author":"Linderman","year":"2022","journal-title":"Nature Communications"},{"key":"2026051804121345000_bib16","doi-asserted-by":"crossref","first-page":"1349","DOI":"10.1038\/s41587-022-01273-7","article-title":"Spatially informed cell-type deconvolution for spatial transcriptomics","volume":"40","author":"Ma","year":"2022","journal-title":"Nature Biotechnology"},{"key":"2026051804121345000_bib17","doi-asserted-by":"crossref","first-page":"3526","DOI":"10.3390\/cancers13143526","article-title":"Cancer-associated fibroblasts: Implications for cancer therapy","volume":"13","author":"Maia","year":"2021","journal-title":"Cancers"},{"key":"2026051804121345000_bib18","doi-asserted-by":"crossref","first-page":"126","DOI":"10.2174\/1874210601206010126","article-title":"Current aspects on oral squamous cell carcinoma","volume":"6","author":"Markopoulos","year":"2012","journal-title":"The Open Dentistry Journal"},{"key":"2026051804121345000_bib19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-022-30033-z","article-title":"Reference-free cell-type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data","volume":"13","author":"Miller","year":"2022","journal-title":"Nature Communications"},{"key":"2026051804121345000_bib20","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1038\/s41587-019-0392-8","article-title":"Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas","volume":"38","author":"Moncada","year":"2020","journal-title":"Nature Biotechnology"},{"key":"2026051804121345000_bib21","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1038\/s41592-022-01409-2","article-title":"Museum of spatial transcriptomics","volume":"19","author":"Moses","year":"2022","journal-title":"Nature Methods"},{"key":"2026051804121345000_bib22","first-page":"51","volume-title":"A Sparse Areal Mixed Model for Multivariate Outcomes, with an Application to Zero-Inflated Census Data","author":"Musgrove","year":"2019"},{"key":"2026051804121345000_bib23","doi-asserted-by":"crossref","first-page":"1611","DOI":"10.1016\/j.cell.2017.10.044","article-title":"Single-cell transcriptomic\u00a0analysis of primary and metastatic tumor ecosystems in head and neck cancer","volume":"171","author":"Puram","year":"2017","journal-title":"Cell"},{"key":"2026051804121345000_bib24","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1126\/science.aaf2403","article-title":"Visualization and analysis of gene expression in tissue sections by spatial transcriptomics","volume":"353","author":"St\u00e5hl","year":"2016","journal-title":"Science"},{"key":"2026051804121345000_bib25","first-page":"1","article-title":"Region-specific denoising identifies spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics data","volume":"13","author":"Wang","year":"2022","journal-title":"Nature Communications"},{"key":"2026051804121345000_bib26","first-page":"3571","article-title":"Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory","volume":"11","author":"Watanabe","year":"2010","journal-title":"Journal of Machine Learning Research"},{"key":"2026051804121345000_bib27","doi-asserted-by":"crossref","first-page":"954","DOI":"10.1093\/biomet\/87.4.954","article-title":"A new family of power transformations to improve normality or symmetry","volume":"87","author":"Yeo","year":"2000","journal-title":"Biometrika"},{"key":"2026051804121345000_bib28","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1016\/j.cell.2018.06.021","article-title":"Molecular architecture of the mouse nervous system","volume":"174","author":"Zeisel","year":"2018","journal-title":"Cell"},{"key":"2026051804121345000_bib29","doi-asserted-by":"crossref","first-page":"1372","DOI":"10.1038\/s41587-023-02019-9","article-title":"Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology","volume":"42","author":"Zhang","year":"2024","journal-title":"Nature Biotechnology"},{"key":"2026051804121345000_bib30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13059-022-02684-0","article-title":"Modeling zero inflation is not necessary for spatial transcriptomics","volume":"23","author":"Zhao","year":"2022","journal-title":"Genome Biology"}],"container-title":["Biometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/biometrics\/article-pdf\/82\/2\/ujag055\/68120695\/ujag055.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/biometrics\/article-pdf\/82\/2\/ujag055\/68120695\/ujag055.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T08:12:31Z","timestamp":1779091951000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/biometrics\/article\/doi\/10.1093\/biomtc\/ujag055\/8657890"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,9]]},"references-count":30,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,4,9]]}},"URL":"https:\/\/doi.org\/10.1093\/biomtc\/ujag055","relation":{},"ISSN":["0006-341X","1541-0420"],"issn-type":[{"value":"0006-341X","type":"print"},{"value":"1541-0420","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,6]]},"published":{"date-parts":[[2026,4,9]]},"article-number":"ujag055"}}