 This makes unsupervised methods, those with no prior assumptions of class membership, a popular first choice.The most common of these methods includes principal component analysis (PCA) which can efficiently reduce the dimensions of a dataset to a few which explain the greatest variation.When analyzed in the lower-dimensional PCA space, clustering of samples with similar metabolic fingerprints can be detected.