TY  - JOUR
A1  - Klann, Kevin
A1  - Münch, Christian
T1  - PBLMM: Peptide-based linear mixed models for differential expression analysis of shotgun proteomics data
T2  - Journal of cellular biochemistry
N2  - 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.
KW  - bioinformatics
KW  - data analysis
KW  - differential expression
KW  - proteomics
KW  - statistics
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/75498
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-754982
SN  - 1097-4644
SN  - 1547-9366
SN  - 1547-1748
N1  - The source code and implementations are made freely accessible via Github under https://github.com/klannk/ mssuite and https://github.com/klannk/Peptide_based_LMM. All proteomics data is will be shared upon request.
VL  - 123
IS  - 3
SP  - 691
EP  - 696
PB  - Wiley-Liss
CY  - New York, NY
ER  -