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Updated: Jan 13, 2026

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UBD: Incorporación de la incertidumbre en las estimaciones de proporciones de tipos celulares de muestras a granel

Youshu Cheng^1,2, Chen Lin¹, Hongyu Li¹

¹Department of Biostatistics, Yale School of Public Health, 47 College St, New Haven, CT 06510, United States.

Briefings in bioinformatics

|January 11, 2026

Abstract:

Statistical deconvolution methods offer a powerful solution for estimating cell-type-specific (CTS) profiles from readily available bulk tissue data. 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.

Palabras clave:

señales específicas de tipos celulares deconvolución incertidumbre en las proporciones de tipos celulares

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