Utilities for quantifying separation in PCA/PLS-DA scores plots

Bradley Worley, Steven Halouska, Robert Powers

Research output: Contribution to journalArticlepeer-review

149 Scopus citations


Metabolic fingerprinting studies rely on interpretations drawn from low-dimensional representations of spectral data generated by methods of multivariate analysis such as principal components analysis and projection to latent structures discriminant analysis. The growth of metabolic fingerprinting and chemometric analyses involving these low-dimensional 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.

Original languageEnglish (US)
Pages (from-to)102-104
Number of pages3
JournalAnalytical Biochemistry
Issue number2
StatePublished - Feb 15 2013


  • MVA
  • Metabolomics
  • PCA
  • PLS-DA

ASJC Scopus subject areas

  • Biophysics
  • Biochemistry
  • Molecular Biology
  • Cell Biology


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