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Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob
Ludger J E Goeminne1, Kris Gevaert2, Lieven Clement3
1Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium; VIB-UGent Center for Medical Biotechnology, VIB, Belgium; Department of Biochemistry, Ghent University, Belgium; Bioinformatics Institute Ghent, Ghent University, Belgium.
Journal of Proteomics
|April 10, 2017
Summary
This tutorial introduces MSqRob, a free R package for analyzing quantitative proteomics data. It helps researchers design experiments and analyze results for more reproducible proteomics studies.
Area of Science:
- Proteomics
- Bioinformatics
- Statistical Analysis
Background:
- Label-free shotgun proteomics generates vast datasets, making data extraction challenging.
- Effective analysis of quantitative proteomics data is crucial for biological insights.
- Existing methods may not adequately address complex experimental designs in proteomics.
Purpose of the Study:
- To provide a foundational tutorial for analyzing quantitative proteomics data.
- To introduce the MSqRob R package for robust relative protein quantification.
- To enhance experimental design and data analysis quality in proteomics.
Main Methods:
- Utilizing the free and open-source R package MSqRob.
- Implementing peptide-level robust ridge regression for protein quantification.
- Demonstrating interactive preprocessing, data analysis, and visualization.
Main Results:
- MSqRob handles diverse experimental proteomics designs.
- The package outputs proteins ranked by statistical significance.
- Interactive features aid in anomaly detection and result validation.
Conclusions:
- MSqRob facilitates higher quality data analysis workflows.
- The tutorial promotes wider adaptation of advanced peptide-based models.
- Well-documented scripts enable automation in cluster environments.