In other words, the left and bottom axes are of the PCA plot - use them to read PCA scores of the samples (dots). You probably notice that a PCA biplot simply merge an usual PCA plot with a plot of loadings. PCA biplot = PCA score plot + loading plot Now that you know all that, reading a PCA biplot is a piece of cake.
See how these vectors are pinned at the origin of PCs (PC1 = 0 and PC2 = 0)? Their project values on each PC show how much weight they have on that PC. A loading plot shows how strongly each characteristic influences a principal component. Such influences, or loadings, can be traced back from the PCA plot to find out what produces the differences among clusters. PCs describe variation and account for the varied influences of the original characteristics.
Instead, it reduces the overwhelming number of dimensions by constructing principal components (PCs). PCA does not discard any samples or characteristics (variables). A PCA plot shows clusters of samples based on their similarity.įigure 1. In a nutshell, PCA capture the essence of the data in a few principal components, which convey the most variation in the dataset. We have answered the question “What is a PCA?” in this jargon-free blog post - check it out for a simple explanation of how PCA works. Principal component analysis ( PCA) has been gaining popularity as a tool to bring out strong patterns from complex biological datasets.