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Fig. 4 | BMC Bioinformatics

Fig. 4

From: Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics

Fig. 4

ROC curves comparing the performance of our pipeline with a method using random forest imputation and linear regression analysis. A single imputation using a random forest algorithm from the R package missForest was compared to our pipeline running 100 imputations. We applied a criterion of either a LFC cut-off, a p-value, or both at the same time. Both, LFC, and P-val indicates which parameters were varied (p-value and LFC at the same time, LFC only, and p-value only, respectively) when creating the ROC curves. LFC was changed from zero to two and p-value from 0.05 to 0.001, simultaneously or separately. When only LFC was changed the p-value was fixed to 0.05, and when the p-value was changed LFC was fixed to zero. “Random Forest” indicates that the missForest packages were used for the imputation and “Normal” indicates that our pipeline was used with limma with normal regression. Y-axis show TPR and x-axis show FPR expressed as a percentage (TPR*100, FPR*100, respectively)

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