Table 3.

A summary of commonly applied multivariate data analysis approaches in metabolomics studies. Abbreviations: PCA, principal component analysis; PLS-DA, partial least squares discriminant analysis; OPLS-DA, orthogonal partial least squares discriminant analysis; HCA, hierarchical cluster analysis; RF, Random Forest.

Technique	Unsupervised / Supervised	Characteristics
PCA	Unsupervised	Exploratory clustering technique extremely useful in identification of differences between observations including variable differences and covariances
PLS-DA	Supervised	Maximum separation between groups of observations is achieved using rotating PCA components. Useful for obtaining information about which variables are involved in class separation
OPLS-DA	Supervised	Systematic variation that is not correlated with classes is removed, which may improve interpretation but not predictivity
HCA	Unsupervised	Exploratory tool to visualize groupings of observations and represented as a tree or dendrogram showing observation homology
RF	Supervised or unsupervised	A learning algorithm which uses an ensemble of decision trees to assign class relationships to observations