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

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.