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Anal Biochem. 2013 Feb 15;433(2):102-4. doi: 10.1016/j.ab.2012.10.011. Epub 2012 Oct 15.

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

Author information

1
Department of Chemistry, University of Nebraska-Lincoln, NE 68588-0304, USA.

Abstract

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.

PMID:
23079505
PMCID:
PMC3534867
DOI:
10.1016/j.ab.2012.10.011
[Indexed for MEDLINE]
Free PMC Article

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