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

J Cell Biochem. 2022 Mar;123(3):691-696. doi: 10.1002/jcb.30225. Epub 2022 Feb 7.


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.

Keywords: bioinformatics; data analysis; differential expression; proteomics; statistics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Linear Models
  • Peptides* / analysis
  • Peptides* / genetics
  • Proteins
  • Proteomics* / methods
  • Software


  • Peptides
  • Proteins