Date of this Version
Anal Biochem. 2013 February 15; 433(2): 102–104. doi:10.1016/j.ab.2012.10.011.
Metabolic fingerprinting studies rely on interpretations drawn from low-dimensional representations of spectral data generated by methods of multivariate analysis such as PCA and PLS-DA. The growth of metabolic fingerprinting and chemometric analyses involving these lowdimensional scores plots necessitates the use of quantitative statistical measures to describe significant differences between experimental groups. Our updated version of the PCAtoTree software provides methods to reliably visualize and quantify separations in scores plots through dendrograms employing both nonparametric and parametric hypothesis testing to assess node significance, as well as scores plots identifying 95% confidence ellipsoids for all experimental groups.