UBD: incorporating uncertainty in cell type proportion estimates from bulk samples to infer cell-type-specific profiles
Year: 2026
Type: article
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.
Source: Briefings in Bioinformatics
DOI: 10.1093/bib/bbaf711
Authors: Youshu Cheng, Chen Lin, Hongyu Li, Ke Xu, Hongyu Zhao
Institutions: Yale University, VA Connecticut Healthcare System
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,885
Awards: Feature selection of DNA methylation biosignatures for neuropathy with comorbid drug abuse in the setting of HIV infection, Computational and Statistical Methods to determine variant effect across cell types and development stages, Cell-type based epigenomic analysis to identify druggable genes for people living with HIV infection and using cannabis, Laboratory, Data Analysis, and Coordinating Center (LDACC) for the Developmental Human Genotype-Tissue Expression Project
OpenAlex ID: W7121228036