Baldur: Bayesian hierarchical modeling for label-free proteomics exploiting gamma dependent mean-variance trends

Berg, P.; Popescu, G.

doi:10.1101/2023.05.11.540411

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bioRxiv · 10.1101/2023.05.11.540411

Baldur: Bayesian hierarchical modeling for label-free proteomics exploiting gamma dependent mean-variance trends

Berg, P.·Popescu, G.

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Abstract

Due to its simplicity in sample preparation, label-free quantification has become de facto in proteomics research at the expense of precision. We propose a Bayesian hierarchical decision model to test for differences in means between conditions for proteins, peptides, and post-translation modifications. We introduce a novel Bayesian regression model to characterize local mean-variance trends in the data to describe measurement uncertainty and to estimate the decision model hyperparameters. Our model vastly improves over state-of-the-art methods (Limma-Trend and t-test) in several spike-in datasets by having competitive performance in detecting true positives while showing superiority by greatly reducing false positives.

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Berg, P., Popescu, G.. 2023-05-14. Baldur: Bayesian hierarchical modeling for label-free proteomics exploiting gamma dependent mean-variance trends. https://doi.org/10.1101/2023.05.11.540411

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