UBD: incorporating uncertainty in cell type proportion estimates from bulk samples to infer cell-type-specific
Youshu Cheng1,2, 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
Summary
This study introduces Uncertainty-aware Bayesian Deconvolution (UBD) to improve cell-type-specific (CTS) profile estimation from bulk tissue data by accounting for uncertainty in cell type proportions. UBD refines these proportions and estimates CTS data simultaneously, enhancing accuracy.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Statistical deconvolution methods estimate cell-type-specific (CTS) profiles from bulk tissue data.
- Existing methods require known cell type proportions, introducing uncertainty when estimates are used as ground truth.
Purpose of the Study:
- To develop a novel method, Uncertainty-aware Bayesian Deconvolution (UBD), that incorporates uncertainty in cell type proportion estimates.
- To refine cell type proportions and simultaneously estimate sample-level CTS data.
Main Methods:
- UBD employs Bayesian deconvolution to explicitly model uncertainty in initial cell type proportion estimates.
- The method refines these proportions and estimates CTS profiles concurrently.
Main Results:
- Extensive simulations demonstrate that UBD significantly improves the accuracy of CTS profile estimation.
- UBD successfully identified additional CTS signals when applied to two real biological datasets.
Conclusions:
- UBD offers a robust approach to address the limitations of existing deconvolution methods by handling uncertainty in cell type proportions.
- This method enhances the reliability and discovery potential of CTS profiles derived from bulk tissue data.
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