Clement, Lieven

Gevaert, Kris

Goeminne, Ludger

Van Quickelberghe, Emmy

Ghent University Academic Bibliography

Wild Type (WT) Knock Out (KO)Time0 Con Con Con Con Con Con1 IS1IS2IS1IS2IS1IS2IS1IS2IS1IS2IS1IS26 IS1IS2IS1IS2IS1IS2IS1IS2IS1IS2IS1IS2Analyzing repeated measures designs in label-free proteomics with MSqRob (MCP 2016 15(2):657-68.)Lieven Clement(3,4,*), Ludger Goeminne(1,2,3,4), Emmy Van Quickelberghe(1,2,4) & Kris Gevaert(1,2,4)1Medical Biotechnology Center, VIB, Ghent, Belgium; 2Department of Biochemistry; 3Department of Applied Mathematics, Computer Science and Statistics;4Bioinformatics institute Ghent, BIG -N2N, Ghent University, Belgium. *Contact: lieven.clement@UGent.beMSqRob workflowResultsImport data (MaxQuant or Mascot)Preprocessing (e.g. log2 transformation, normalization, ...)Robust peptide-based regressionTest contrasts of interest(FDR-corrected p-values)Background: In repeated measures designs different observations are obtained on the same experimentalunit (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 typicallymore similar than data between EUs. Most existing workflows cannot address experiments with complexdesigns 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).Repeated Measures DesignConclusion - Powerful analysis of complex designs with fixed and random effects- Robust estimation and shrinkage of fixed effects- Data exploration and visualisation - Download MSqRob package: https://github.com/ludgergoeminne/MSqRobProtein with significant upregulation in KO vs WT at baseline, 1h upon treatment with IS1 stimulusand 1h and 6h with IS2, and a significant interaction, i.e. the upregulation in KO decreases over time.  Model log2 Intensity =G×IS×T+(1|mouse)+(1|run)+(1|Peptide)+εMSqRob GUI Output Additional PlotLog2 peptide intensity modeled by genotype (G), Inflammatory Stimulus (IS) & time (T) main effects & interactions + random effects for mouse, run and peptide to address correlation. Normal error.Baseline control, early and late responses on inflammatory stimuli (IS)

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

https://biblio.ugent.be/publication/8616176/file/8616178.pdf

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

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Ghent University Academic Bibliography