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Related Experiment Videos

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
PubMed
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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.
Keywords:
BiostatisticsDifferential protein abundanceExperimental designLabel-free quantificationPeptide-based linear modelTandem mass spectrometry

Related Experiment Videos

  • 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.