PBLMM: Peptide-based linear mixed models for differential expression analysis of shotgun proteomics data

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.

Automated summary

We introduce a peptide-based linear mixed models tool, PBLMM, as a standalone desktop application for differential expression analysis of proteomics data. We also offer a Python package that enables streamlined data analysis workflows implementing the PBLMM algorithm. PBLMM, which uses peptide-based linear mixed regression models, is user-friendly and does not require scripting experience. Our study demonstrates that peptide-based models outperform classical methods in statistically inferring differentially expressed proteins. Furthermore, PBLMM exhibits superior statistical power, especially in situations with low effect size and/or low sample size.