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Updated: Sep 19, 2025
Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification
Published on: November 15, 2017
msqrob2TMT: Robust Linear Mixed Models for Inferring Differential Abundant Proteins in Labeled Experiments With
Stijn Vandenbulcke1, Christophe Vanderaa2, Oliver Crook3
1Department of Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium; CompOmics, VIB Center for Medical Biotechnology, VIB, Ghent, Belgium; Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, Ghent, Belgium.
New workflows called msqrob2TMT improve differential abundance analysis for complex mass spectrometry-based proteomics experiments. This tool enhances statistical inference and biomarker discovery by effectively handling intricate sample correlations.
Area of Science:
- Proteomics
- Computational Biology
- Statistical Modeling
Background:
- Mass spectrometry-based proteomics utilizes labeling strategies to increase sample throughput via multiplexed runs.
- Complex experimental designs in proteomics often exceed single-run capacity, leading to correlation structures that complicate statistical inference and biomarker discovery.
Purpose of the Study:
- To introduce msqrob2TMT, a suite of mixed model-based workflows for differential abundance analysis in labeled mass spectrometry-based proteomics data.
- To provide a flexible and modular tool that accommodates complex experimental designs and corrects for feature-specific covariates.
Main Methods:
- Development of mixed model-based workflows (msqrob2TMT) for differential abundance analysis.
- Accommodation of sample-specific and feature-specific covariates for complex experimental designs.
- Benchmarking against DEqMS, MSstatsTMT, and msTrawler using spike-in studies and a real mouse study.
Main Results:
- msqrob2TMT demonstrates greater flexibility, improved modularity, and enhanced performance compared to existing tools.
- Robust ridge regression application contributes to improved performance.
- Successful application in a real mouse study, effectively accounting for complex correlation structures.
Conclusions:
- msqrob2TMT is a powerful and flexible tool for differential abundance analysis in complex mass spectrometry-based proteomics studies.
- The workflows facilitate reliable statistical inference and biomarker discovery in challenging experimental designs.
- The tool effectively addresses complex correlation structures inherent in large-scale proteomics data.
