What are Principal Components analyzed in Rotor-Gene ScreenClust HRM Software?

Principal component analysis is a well-established method of data analysis for multivariate data sets, such as obtained from, e.g.,  microarray analysis or image analysis. However, it is new in ScreenClust for HRM data. Principal Components (PCs) are extracted from the residuals plot so that the first Principle Component (PC1) represents the greatest variability or difference between all samples. The second (PC2) represents the regions of difference not already present in PC1. The third (PC3) represents differences not in PC1 and PC2.