Advanced search
1 file | 2.33 MB

MSqRob: analysis of label-free proteomics data in an R/Shiny environment

Ludger Goeminne (UGent) , Emmy Van Quickelberghe (UGent) , Kris Gevaert (UGent) and Lieven Clement (UGent)
(2017)
Author
Organization
Keywords
MSqRob, repeated measures designs, complex designs, statistics, biostatistics, data analysis, differential protein abundance, peptide-based linear model, ridge regression, M estimation, Huber weights, empirical Bayes variance estimation, label-free quantification, differential proteomics, peptide-based linear model, tandem mass spectrometry

Downloads

  • presentation ProteomicForum 2017 final.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 2.33 MB

Citation

Please use this url to cite or link to this publication:

Chicago
Goeminne, Ludger, Emmy Van Quickelberghe, Kris Gevaert, and Lieven Clement. 2017. “MSqRob: Analysis of Label-free Proteomics Data in an R/Shiny Environment.” In .
APA
Goeminne, L., Van Quickelberghe, E., Gevaert, K., & Clement, L. (2017). MSqRob: analysis of label-free proteomics data in an R/Shiny environment. Presented at the Proteomic Forum 2017.
Vancouver
1.
Goeminne L, Van Quickelberghe E, Gevaert K, Clement L. MSqRob: analysis of label-free proteomics data in an R/Shiny environment. 2017.
MLA
Goeminne, Ludger et al. “MSqRob: Analysis of Label-free Proteomics Data in an R/Shiny Environment.” 2017. Print.
@inproceedings{8616179,
  author       = {Goeminne, Ludger and Van Quickelberghe, Emmy and Gevaert, Kris and Clement, Lieven},
  language     = {eng},
  location     = {Potsdam},
  title        = {MSqRob: analysis of label-free proteomics data in an R/Shiny environment},
  year         = {2017},
}