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Table 2 Statistics for PLS-DA, OPLS-DA model, details for predictive and orthogonal parts; ROC analysis

From: Platelet protein biomarker panel for ovarian cancer diagnosis

A) PLS-DA-based analysis of protein spots expression in 2D
 Component Latent variables R2X (cum) Q2 (cum) CV-ANOVA, p-value permutation test, p-value    Sensitivity Specificity
 Model 3 0.318 0.72 8,5 * 10–9 <0.001   calibration set 0.96 0.88
        validation set 1.00 0.44
B) OPLS-DA-based analysis of protein expression in western blot.
 Component Latent variables R2X (cum) R2 (cum) Q2 (cum) CV-ANOVA, p-value permutation test, p-value   Sensitivity Specificity
 Model 1 + 1 0.203 0.632 0.477 4.41E-14 <0.001 calibration set 0.83 0.89
 Predictive 1 0.0717 0.632 0.477    validation set 0.88 not tested
 Orthogonal 1 0.131 0       
C) ROC analysis of protein expression in western blot.
 Compared groups AUC standart deviation 95% confidence interval z statistics p-value    Sensitivity Specificity
 ROC 1 0.777 0.0418 0,695 to 0,859 6.639 <0,0001   ROC1 60 83.33
 ROC 2 0.831 0.0501 0,733 to 0,930 6.615 <0,0001   ROC2 83.33 76.19
D) OPLS-DA-based analysis of protein expression in Digi west.
 Component Latent variables R2X (cum) R2 (cum) Q2 (cum) CV-ANOVA, p-value permutation test, p-value   Sensitivity Specificity
 Model 1 + 2 0.24 0.785 0.345 4.50E-03 <0.001 test set 0.7 0.83
 Predictive 1 0.037 0.785 0.345      
 Orthogonal 2 0.203        
  1. R2X cumulative percentage of X variance explained, R2 cumulative percentage of Y variance explained, Q2 cumulative percentage of variance of Y predicted, CV-ANOVA p-value p-value of cross-validation ANOVA, permutaion test p-value p-value of (1000 iterations) permutation test