Figure 4

Principal component and discrimination analysis of top expressive glycans in T2DM and healthy controls. Feature selections are important in the refinement of biological and biochemical hypotheses. We identified a combination of discriminative features from a disparate block of N-glycan data set. N-glycan peaks loaded differently along the two principal components (PC), with estimates of positive and negative weights. A large absolute value indicates the importance of the variable to the PC and the colour codes indicate how prominent the biomarker expressed in T2DM and healthy controls. Selected variables were ranked from the least important to the most important. The classification accuracy the training and testing of the discriminant function formulated using the 10 glycans associated with component 1 reveals a model with good learning rate.