OPLS discriminant analysis: combining the strengths of PLS‐DA and SIMCA classification

By Max Bylesjö, Mattias Rantalainen, Olivier Cloarec, Jeremy K. Nicholson, Edward C. Holmes, Johan Trygg. 2006

1,406 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Industrial and Manufacturing Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1774f8a51ec1abb355c2d910ad966a0a5f32e70d

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In Analytical Chemistry

In Industrial and Manufacturing Engineering

In Statistics and Probability

Citations in the corpus

Multivariate paired data analysis: multilevel PLSDA versus OPLSDA (2009, methodology): “OPLSDA was introduced as an improvement of the PLSDA method to discriminate two or more groups (classes) using multivariate data (Bylesjo et al. 2006; Trygg and Wold 2002).”
A chemometrics toolbox based on projections and latent variables (2014, methodology): “Partial least squares DA [42] and OPLS-DA [43] are both based on the analogy between LDA and MLR with Y being a dummy matrix expressing the class assignment of the training set using one 0/1 dummy variable per class.”
Multiclass partial least squares discriminant analysis: Taking the right way—A critical tutorial (2018, neutral): “dcrit 1⁄4 χ 1–α;K–1 ð Þ: (14) where χ is the quantile of the chi‐squared distribution with K − 1 degrees of freedom.”
Multivariate Analysis in Metabolomics. (2012, methodology): “The predictive OPLS-DA scores and loadings used to estimate Y are then composed of variation directly correlated with Y and free of interfering structured variation, yielding enhanced interpretability when compared with PLS-DA [43, 52].”
Analytical Methods in Untargeted Metabolomics: State of the Art in 2015 (2015, neutral): “Classification of metabolomics samples is commonly performed by fitting the discriminant analysis versions of PLS and O-PLS models (i.e., PLS-DA, O-PLS-DA; Kemsley, 1996; Bylesjö et al., 2006).”
A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. (2015, neutral): “orthogonal partial least squares discriminant analysis [157] M”
A consensus orthogonal partial least squares discriminant analysis (OPLS-DA) strategy for multiblock Omics data fusion. (2013, methodology): “ts discriminant analysis counterpart (OPLS-DA) was demonstrated s a powerful tool for the analysis of qualitative data structures, hile the prediction results are equivalent to classification using tandard PLS-DA [6].”
Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources (2020, methodology): “We note that both OPLS-DA and SPLS-DA suffer from the same overfitting drawback as PLS-DA.”