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However, a critical limitation of existing methods is that they require the knowledge of cell type proportions of individuals in the bulk data. While the ground truth of cell type proportions in bulk samples are unknown, those methods use the estimated proportions to approximate the truth, which potentially introduces additional uncertainties in the inferred CTS profiles. To address this challenge, we propose Uncertainty-aware Bayesian Deconvolution (UBD) to incorporate uncertainty in cell type proportion estimates. By explicitly modeling the uncertainty in the initial estimates, UBD refines cell type proportions and estimates sample-level CTS data simultaneously. We show that UBD can improve the estimates of CTS profiles through extensive simulations. We further demonstrate the utility of UBD to reveal more CTS signals in its applications to two real datasets.<\/jats:p>","DOI":"10.1093\/bib\/bbaf711","type":"journal-article","created":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T09:41:14Z","timestamp":1768124474000},"source":"Crossref","is-referenced-by-count":1,"title":["UBD: incorporating uncertainty in cell type proportion estimates from bulk samples to infer cell-type-specific profiles"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6398-2082","authenticated-orcid":false,"given":"Youshu","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Biostatistics, Yale School of Public Health , 47 College St, New Haven, CT 06510 ,","place":["United States"]},{"name":"VA Connecticut Healthcare System , 950 Campbell Ave, West Haven, CT 06516 ,","place":["United 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