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In univariate analysis, body weight and gender discriminated the two groups.
Only body weight and gender discriminated the two groups of patients.
To further identify early gene expression changes that best discriminated the two groups after one cycle of chemotherapy, we also performed a parametric t-test on each gene's log-ratio change value from each patient.
O2PLS-DA of the TAG profile produced a robust model that readily discriminated the two groups (R(X) = 63.9%; R(Y) = 99.3%; Q = 98.1%; Fig. 4e), and the model readily passed cross-validation using CV-ANOVA (p = 7 × 10−12).
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Here, linear discriminant analysis (LDA) and orthogonal corrected PLS-based discriminant analysis (OPLSDA) [39] [40] were used to discriminate the two groups of mice submitted to a time-course study of the effects of maize protein extracts.
Results show that interference management is the most likely variable to discriminate the two groups.
The SVM classification analysis gave clear-cut patterns that discriminate the two groups.
We used a support vector machine (SVM) classification, extracting the mean metabolism from the metabolic patterns, or networks, that discriminate the two groups.
The nu-SVM classification analysis had a good generalization and could discriminate the two groups with an accuracy of 65% (computed by n-fold cross validation, greater than chance >57%).
The threshold score discriminating the two groups is zero.
This methods selects uncorrelated genes which best discriminate the two groups in internal cross-validation.
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