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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)
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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

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Citation

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

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