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Analyzing repeated measures designs in label-free proteomics with MSqRob (MCP 2016 15(2):657-68.)

Lieven Clement (UGent) , Ludger Goeminne (UGent) , Emmy Van Quickelberghe (UGent) and Kris Gevaert (UGent)
(2017)
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Abstract
In repeated measures designs different observations are obtained on the same experimental unit (EU), which increases statistical power for within subject treatment effects because the between-subject variability can be eliminated from the estimation. Data of the same EU, however, are typically more similar than data between EUs. Most existing workflows cannot address experiments with complex designs and correlation, resulting in a power loss when assessing treatment effects within EU (e.g. compound effects) and improper error rate control for effects between EU (e.g. KO vs WT).
Keywords
MSqRob, repeated measures designs, fixed effects, random effects, complex designs, statistics, biostatistics, data analysis, differential protein abundance, 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:

Chicago
Clement, Lieven, Ludger Goeminne, Emmy Van Quickelberghe, and Kris Gevaert. 2017. “Analyzing Repeated Measures Designs in Label-free  Proteomics with MSqRob (MCP 2016 15(2):657-68.).” In .
APA
Clement, L., Goeminne, L., Van Quickelberghe, E., & Gevaert, K. (2017). Analyzing repeated measures designs in label-free  proteomics with MSqRob (MCP 2016 15(2):657-68.). Presented at the 2017 EuBIC Winter School.
Vancouver
1.
Clement L, Goeminne L, Van Quickelberghe E, Gevaert K. Analyzing repeated measures designs in label-free  proteomics with MSqRob (MCP 2016 15(2):657-68.). 2017.
MLA
Clement, Lieven et al. “Analyzing Repeated Measures Designs in Label-free  Proteomics with MSqRob (MCP 2016 15(2):657-68.).” 2017. Print.
@inproceedings{8616176,
  abstract     = {In repeated measures designs different observations are obtained on the same experimental unit (EU), which increases statistical power for within subject treatment effects because the between-subject variability can be eliminated from the estimation. Data of the same EU, however, are typically more similar than data between EUs. Most existing workflows cannot address experiments with complex designs and correlation, resulting in a power loss when assessing treatment effects within EU (e.g. compound effects) and improper error rate control for effects between EU (e.g. KO vs WT).},
  author       = {Clement, Lieven and Goeminne, Ludger and Van Quickelberghe, Emmy and Gevaert, Kris},
  language     = {eng},
  location     = {Semmering},
  title        = {Analyzing repeated measures designs in label-free  proteomics with MSqRob (MCP 2016 15(2):657-68.)},
  year         = {2017},
}