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

Ludger Goeminne (UGent) , Kris Gevaert (UGent) and Lieven Clement (UGent)
(2017)
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Abstract
In MS-based proteomics, proteins are not completely covered and peptides that are identified in one sample are often missing in other samples. Common workflows adopt software tools that have graphical user interfaces, but are often based on less sensitive protein level abundance values and/or provide inefficient or even inappropriate statistical inference. MSqRob is an R package that accounts for peptide-specific effects as well as differences in the number of peptide identifications. It copes with overfitting, unstable variances and outliers by three modular extensions: (1) ridge regression, (2) empirical Bayes variance estimation and (3) M-estimation. MSqRob provides state-of-the-art statistical inference for label-free proteomics experiments with simple and complex designs: MSqRob can cope with multifactorial, block, repeated measures and time series designs, which cannot be analyzed properly in existing proteomics data analysis software. The Shiny graphical user interface for MSqRob is very user-friendly and requires no statistical programming experience. Goeminne, L.J.E., Gevaert, K. and Clement, L. Molecular and Cellular Proteomics 15(2), pp 567-668. Download MSqRob: https://github.com/ludgergoeminne/MSqRob
Keywords
MSqRob, R, Shiny, statistics, biostatistics, data analysis, differential protein abundance, label-free quantification, differential proteomics, peptide-based linear model, robust ridge regression, M estimation, Huber weights, empirical Bayes variance estimation, tandem mass spectrometry, overfitting, outliers, missing values

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Citation

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

Chicago
Goeminne, Ludger, Kris Gevaert, and Lieven Clement. 2017. “MSqRob: Analysis of Label-free Proteomics Data in an R/Shiny Environment.” In .
APA
Goeminne, L., Gevaert, K., & Clement, L. (2017). MSqRob: analysis of label-free proteomics data in an R/Shiny environment. Presented at the 2017 EuBIC Winter School.
Vancouver
1.
Goeminne L, Gevaert K, Clement L. MSqRob: analysis of label-free proteomics data in an R/Shiny environment. 2017.
MLA
Goeminne, Ludger, Kris Gevaert, and Lieven Clement. “MSqRob: Analysis of Label-free Proteomics Data in an R/Shiny Environment.” 2017. Print.
@inproceedings{8616173,
  abstract     = {In MS-based proteomics, proteins are not completely covered and peptides that are identified in one sample are often missing in other samples. Common workflows adopt software tools that have graphical user interfaces, but are often based on less sensitive protein level abundance values and/or provide inefficient or even inappropriate statistical inference.

MSqRob is an R package that accounts for peptide-specific effects as well as differences in the number of peptide identifications. It copes with overfitting, unstable variances and outliers by three modular extensions: (1) ridge regression, (2) empirical Bayes variance estimation and (3) M-estimation. MSqRob provides state-of-the-art statistical inference for label-free proteomics experiments with simple and complex designs: MSqRob can cope with multifactorial, block, repeated measures and time series designs, which cannot be analyzed properly in existing proteomics data analysis software. The Shiny graphical user interface for MSqRob is very user-friendly and requires no statistical programming experience.

Goeminne, L.J.E., Gevaert, K. and Clement, L. Molecular and Cellular Proteomics 15(2), pp 567-668.
Download MSqRob: https://github.com/ludgergoeminne/MSqRob},
  author       = {Goeminne, Ludger and Gevaert, Kris and Clement, Lieven},
  language     = {eng},
  location     = {Semmering},
  title        = {MSqRob: analysis of label-free proteomics data in an R/Shiny environment},
  year         = {2017},
}