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PBLMM: Peptide-based Linear Mixed Models for Differential Expression Analysis of Shotgun Proteomics Data

Kevin KlannChristian Münch

出版	Wiley-Liss, 2022
URL	http://books.google.com.hk/books?id=dZ5e0AEACAAJ&hl=&source=gbs_api

註釋

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.