Orthogonal PLS-DA (OPLS-DA) is a supervised discriminant analysis statistical method, which is different from the PAC method. CD Mitochondria uses this method to establish a model of the relationship between metabolite expression and sample categories by partial least square regression to predict sample categories.
In OPLS-DA, a regression model is constructed between multivariate data and response variables that contain only class information. The obvious advantage of OPLS-DA over PLS-DA (Partial Least-Squares Discriminant Analysis) is that a single component can be used as a predictive variable for a category, while other components describe changes that are orthogonal to the first predictive component.
CD Mitochondria establishes a pairwise OPLS-DA model, and the parameter evaluation of the model is provided in tabular form. At the same time, the influence and explanatory ability of the expression patterns of each metabolite on the classification of each sample are measured by calculating the variable importance for the projection (VIP), to assist the screening of marker metabolites (usually the VIP value > 1.0 is used as the screening criterion).
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