Figure 7
From: Factors affecting the accuracy of urine-based biomarkers of BSE
Principle component analysis of the samples grouped according to disease state. The 27 samples of the merged set were divided into two groups. One group contained the 14 BSE negative samples and the other group contained the 13 BSE positive samples. The 42 proteins present in 80% of the gels and exhibiting significant differential abundance (ANOVA p ≤ 0.01) were analyzed using the PLSS and RDA algorithms to identify those proteins best able to differentiate between the samples base upon the disease state of the animal that produced them. RDA was used to generate a classifier out of the 10 selected proteins that were able to correctly classify the samples with 96.7 ± 7.5% accuracy. The one misclassified sample, known set sample 22, is identified by an arrow. (PC1 = 48.4, PC2 = 26.0).