In contrast to principal component analysis (PCA), both partial least squares discrimination analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) are supervised statistical methods for discriminative analysis. These methods develop models that correlate metabolite expression levels with sample categories to facilitate prediction of the category of samples.
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