Tools for quantifying separation in PCA/PLS-DA scores

Principal Component Analysis (PCA) and Projection to Latent Structures Discriminant Analysis (PLS-DA) are two of the most widely used methods in metabolomics, chemometrics and data discovery in general. Once a validated model has been generated, separations between groups in scores space may be used to infer experimental conclusions. However, no standard method of exists to quantitatively discuss scores-space separations. We have developed a set of tools (our PCA/PLS-DA utilities, or "pca-utils" for short) that allow for quickly and quantitatively ascertaining information about group separations in scores, using both p-value calculations and bootstrap statistics to place numerical values on distances between experimental groups. Our software may be used to generate simple tables of distances or p-values, dendrograms illustrating group relationships, and scores plots annotated with 95% confidence ellipsoids informing group membership.

The PCA/PLS-DA utilities are available on GitHub at the link below. Instructions for cloning and installing from source, as well as instructions for general use, are available there.

• PCA/PLS-DA utilities GitHub page