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Related Experiment Video

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PLS2 in Metabolomics.

Matteo Stocchero1,2, Emanuela Locci3, Ernesto d'Aloja4

  • 1Department of Women's and Children's Health, University of Padova, 35128 Padova (PD), Italy. matteo.stocchero@unipd.it.

Metabolites
|March 20, 2019
PubMed
Summary
This summary is machine-generated.

This study presents Projection to Latent Structures regression 2 (PLS2), a key tool in metabolomics data analysis. It addresses limitations of PLS2, especially with

Keywords:
PLS-DAorthogonally-constrained PLS2post-transformation of PLS2projection to latent structures regression

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Area of Science:

  • Metabolomics
  • Chemometrics
  • Bioinformatics

Background:

  • Metabolomics generates high-dimensional data with correlated variables and redundancy.
  • Discovering biological insights from complex metabolomic datasets is challenging.
  • Projection to Latent Structures regression (PLS) is a widely used multivariate analysis technique in metabolomics.

Purpose of the Study:

  • To provide a comprehensive overview of PLS2 for metabolomics.
  • To discuss limitations of standard PLS2, particularly concerning 'structured noise'.
  • To present recent advancements and strategies for improving PLS2 in metabolomic data analysis.

Main Methods:

  • Mathematical framework of PLS2.
  • Post-transformation procedure for model interpretation.
  • Orthogonally-constrained PLS2 for incorporating experimental design constraints.

Main Results:

  • Demonstration of PLS2 and its enhanced methods on two experimental metabolomic datasets.
  • Illustrates practical application and effectiveness of the discussed techniques.
  • Highlights the utility of post-transformation and orthogonally-constrained PLS2.

Conclusions:

  • PLS2 is a fundamental tool in metabolomics, but requires careful application.
  • Recent improvements enhance PLS2's robustness and interpretability in complex datasets.
  • The presented methods offer practical solutions for analyzing challenging metabolomic data.