Principal Components Analysis:

       PCA is an unsupervised method aiming to find the directions of maximum variance in a data set (X) without referring to the class labels (Y). Furthermore, Euclidean distance underlying PCA allows analysis of the strength and statistical significance that a categorical or numerical covariate (Y) has in explaining variation in a data set (X), such as PERMANOVA, ANOSIM.