CCP
    

Class

Sensitivity

Specificity

PPV

NPV


HSC

0.92

0.943

0.939

0.926

LSC

0.943

0.92

0.926

0.939

BCC
    
Class

Sensitivity

Specificity

PPV

NPV

HSC

0.88

0.887

0.88

0.887

LSC

0.887

0.88

0.887

0.88

LDA
    
Class

Sensitivity

Specificity

PPV

NPV

HSC

0.9

0.943

0.938

0.909

LSC

0.943

0.9

0.909

0.938

NC
    
Class

Sensitivity

Specificity

PPV

NPV

HSC

0.9

0.962

0.957

0.911

LSC

0.962

0.9

0.911

0.957

SVM
    
Class

Sensitivity

Specificity

PPV

NPV

HSC

0.96

0.962

0.96

0.962

LSC

0.962

0.96

0.962

0.96

 Sensitivity and specificity of compound covariate predictor (CCP), Bayesian compound covariate predictor (BCC), linear discriminator analysis (LDA), nearest centroid classification (NC), and support vector machines (SVM). Sensitivity is the probability for a class A sample to be correctly predicted as class A. Specificity is the probability for a non class A sample to be correctly predicted as nonA. Positive Predictive Value (PPV) is the probability that a sample predicted as class A actually belongs to class A. Negative Predictive Value (NPV) is the probability that a sample predicted as non class A actually does not belong to class A.
 For some class high S100A8 cluster (HSC), if n11 = number of class HSC samples predicted as HSC, n12 = number of class HSC samples predicted as low S100A8 cluster (LSC), n21 = number of LSC samples predicted as HSC, and n22 = number of LSC samples predicted as LSC, then the following parameters can characterize performance of classifiers: Sensitivity = n11/(n11+n12), Specificity = n22/(n21+n22), PPV = n11/(n11+n21), and NPV = n22/(n12+n22).