Utilities for quantifying separation in PCA/PLS-DA scores plots
Open accessAbstract
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.
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There are many supervised methods, with partial least-squares discriminant analysis (PLS-DA) or its variants being the most commonly used for comparing two groups.
“There are a large number of supervised methods (120, 127), with the most commonly used analysis for comparing 2 groups being partial least-squares discriminant analysis (PLS-DA) (132) or one of its several variants.”
From The food metabolome: a window over dietary exposure (2014) · cites this paper as [127]
Multivariate analysis is most commonly used for explorative analysis of metabolic profiling data.
“Multivariate analysis is most commonly used for explorative analysis of metabolic profiling data (127).”
From The food metabolome: a window over dietary exposure (2014) · cites this paper as [127]
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- Authors
- Worley, B., Halouska, S., & Powers, R.
- Affiliations
- University of Nebraska–Lincoln (US)
- Corresponding author
- Powers, Robert
- Venue
- Analytical Biochemistry
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 433
- Issue
- 2
- Pages
- 102-104
- ISSN
- 0003-2697 (ISSN-L)
- PMC
- PMC3534867
- OpenAlex
- W2159118331
- Cited by
- 214
- References
- 24
- Citations by year
- 2026: 14 · 2025: 14 · 2024: 19 · 2023: 12 · 2022: 13 · 2021: 22 · 2020: 16 · 2019: 19 · 2018: 15 · 2017: 14 · 2016: 20 · 2015: 10 · 2014: 15 · 2013: 10 · 2012: 1
- FWCI
- 4.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Analytical Biochemistry (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Lincoln (University of Nebraska) (submittedVersion)
- Funders
- National Institutes of Health (RO1 AI087668, R21 AI087561, RR015468-01)
- NIH National Center for Research Resources (P20 RR-17675)
- America Heart Association (0860033Z)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Keywords
- metabolic fingerprinting, principal component analysis, PLS-DA, dendrogram, confidence ellipsoids, chemometrics software
- MeSH terms
- Models, Theoretical, Software, Metabolomics, Metabolome
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