Partial Least Squares Discrimination Analysis (PLS-DA) is a supervised discriminant analysis statistical method, a chemometrics technique used to optimize separation between different groups of samples, which is completed by linking two data matrices X (i.e., raw data) and Y (i.e., groups, class membership, etc.). This method aims to maximize the covariance between the independent variables X (sample readings, the metabolomics data) and the corresponding dependent variable Y (groups or classes) of highly multidimensional data by finding a linear subspace of the explanatory variables.
PLS-DA algorithm has many advantages in dealing with multivariate data. 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, and linear discriminant analysis is carried out by using all non-zero components.
CD Mitochondria established a PLS-DA model for pairwise comparison, and the parameter evaluation obtained from the model is provided in a table. The influence and explanatory ability of the expression patterns of each metabolite on the classification of each group are measured by calculating the variable importance for the projection (VIP), thus assisting the screening of marker metabolites (usually taking VIP > 1.0 as the screening criterion).
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