![]() It extracts the fundamental structure of the data without the need to build any model to represent it. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. 5.6 Plot the entire project on a single panel.5.5 Correlate the principal components back to the clinical data.5.4 Determine the variables that drive variation among each PC. ![]() 5.3 Quickly explore potentially informative PCs via a pairs plot.5.2.6 Colour by a continuous variable and plot other PCs.5.2.5 Modify line types, remove gridlines, and increase point size. ![]()
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