title: Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob.
abstract: Label-free shotgun proteomics is routinely used to assess proteomes. However, extracting relevant information from the massive amounts of generated data remains difficult. This tutorial provides a strong foundation on analysis of quantitative proteomics data. We provide key statistical concepts that help researchers to design proteomics experiments and we showcase how to analyze quantitative proteomics data using our recent free and open-source R package MSqRob, which was developed to implement the peptide-level robust ridge regression method for relative protein quantification described by Goeminne et al. MSqRob can handle virtually any experimental proteomics design and outputs proteins ordered by statistical significance. Moreover, its graphical user interface and interactive diagnostic plots provide easy inspection and also detection of anomalies in the data and flaws in the data analysis, allowing deeper assessment of the validity of results and a critical review of the experimental design. Our tutorial discusses interactive preprocessing, data analysis and visualization of label-free MS-based quantitative proteomics experiments with simple and more complex designs. We provide well-documented scripts to run analyses in bash mode on GitHub, enabling the integration of MSqRob in automated pipelines on cluster environments (https://github.com/statOmics/MSqRob).
  
  
  
  The concepts outlined in this tutorial aid in designing better experiments and analyzing the resulting data more appropriately. The two case studies using the MSqRob graphical user interface will contribute to a wider adaptation of advanced peptide-based models, resulting in higher quality data analysis workflows and more reproducible results in the proteomics community. We also provide well-documented scripts for experienced users that aim at automating MSqRob on cluster environments.
authors.affiliation: Department 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. Electronic address: ludger.goeminne@UGent.vib.be.
authors.forename: Ludger J E
authors.initials: LJE
authors.lastname: Goeminne
authors.affiliation: VIB-UGent Center for Medical Biotechnology, VIB, Belgium; Department of Biochemistry, Ghent University, Belgium; Bioinformatics Institute Ghent, Ghent University, Belgium. Electronic address: kris.gevaert@UGent.vib.be.
authors.forename: Kris
authors.initials: K
authors.lastname: Gevaert
authors.affiliation: Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Belgium; Bioinformatics Institute Ghent, Ghent University, Belgium. Electronic address: lieven.clement@UGent.be.
authors.forename: Lieven
authors.initials: L
authors.lastname: Clement
mesh_terms: D001426:Bacterial Proteins / Q000032:analysis; D002853:Chromatography, Liquid / Q000379:methods*; D019295:Computational Biology; D005604:Francisella tularensis / Q000737:chemistry; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods*; D010455:Peptides / Q000032:analysis; D011506:Proteins / Q000032:analysis*; D020543:Proteome / Q000032:analysis*; D040901:Proteomics / Q000379:methods*; D012984:Software
chemical_list: D001426:Bacterial Proteins; D010455:Peptides; D011506:Proteins; D020543:Proteome
keywords: Biostatistics; Differential protein abundance; Experimental design; Label-free quantification; Peptide-based linear model; Tandem mass spectrometry