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Scientific article
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English

A unified strategy to rebalance multifactorial designs with unequal group sizes: application to analysis of variance multiblock orthogonal partial least squares

Published inAnalytica chimica acta, vol. 1263, p. 341284
Publication date2023-07
Abstract

Adequately handling unbalanced groups remains one of the major challenges for the analysis of multivariate data collected from multifactorial experimental designs. While partial least squares-based methods, such as analysis of variance multiblock orthogonal partial least squares (AMOPLS), can offer better discrimination between factor levels, they can be more heavily affected by this issue, and unbalanced designs of experiments may lead to a substantial confusion of the effects. Even state-of-the-art analysis of variance (ANOVA) decomposition methodologies using general linear models (GLM) lack the ability to efficiently disentangle these sources of variation when combined with AMOPLS.

eng
Keywords
  • Metabolomics
  • Research Design
  • High-dimensional
  • Rebalancing
  • Unbalanced experimental designs
Citation (ISO format)
DE FIGUEIREDO, Miguel, RUDAZ, Serge, BOCCARD, Julien. A unified strategy to rebalance multifactorial designs with unequal group sizes: application to analysis of variance multiblock orthogonal partial least squares. In: Analytica chimica acta, 2023, vol. 1263, p. 341284. doi: 10.1016/j.aca.2023.341284
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ISSN of the journal0003-2670
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