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Here, we present a peptide-based linear mixed models tool-PBLMM, a standalone desktop application for differential expression analysis of proteomics data. We also provide a Python package that allows streamlined data analysis workflows implementing the PBLMM algorithm. PBLMM is easy to use without scripting experience and calculates differential expression by peptide-based linear mixed regression models. We show that peptide-based models outperform classical methods of statistical inference of differentially expressed proteins. In addition, PBLMM exhibits superior statistical power in situations of low effect size and/or low sample size. Taken together our tool provides an easy-to-use, high-statistical-power method to infer differentially expressed proteins from proteomics data. © 2022 The Authors. Journal of Cellular Biochemistry published by Wiley Periodicals LLC.


Kevin Klann, Christian Münch. PBLMM: Peptide-based linear mixed models for differential expression analysis of shotgun proteomics data. Journal of cellular biochemistry. 2022 Mar;123(3):691-696

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PMID: 35132673

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