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New formulation for multi-block-partial least squares discriminant analysis

Abstract : In Chemometrics, the coupling of different kinds of measurements including genomics, proteomics and metabolomics generates a large amount of variables structured into meaningful blocks for the characterization of the same set of samples. Dealing with multi-blocks data in a discrimination scope, we propose, herein, to extend the PLS method to discrimination (PLS-DA), considering the decomposition of the between groups covariance matrix in the multi-block context. This leads to the simultaneous determination of global and block components. This method is illustrated on a case study pertaining to the LACATOL project (registered to the French Clinical Trial under N° NCT01493063) which aims at ensuring the optimal growth of preterm newborns through a personalized nutrition. A multi-block PLS-DA is performed to identify two phenotypes of milk, associated with a growth group (normal vs slow) of preterm newborns. The relationships between metabolomics, free amino acids and macronutriments are highlighted.
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Conference poster
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Submitted on : Friday, June 5, 2020 - 8:48:41 PM
Last modification on : Friday, August 5, 2022 - 8:45:00 AM


  • HAL Id : hal-02801394, version 1
  • PRODINRA : 350153


Véronique Cariou, El Mostafa Qannari, Mohamed Soumah, Marie Cécile Alexandre-Gouabau, Thomas Moyon. New formulation for multi-block-partial least squares discriminant analysis. AgroStat 2016 Congress - 14. Symposium on Statistical Methods for the Food Industry, Mar 2016, Lausanne, Switzerland. 2016. ⟨hal-02801394⟩



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