2.7. OPLS-DA

As an independent approach to validating which proteins may mediate the biological effects of 4-oxo-DHA, we used orthogonal projections to latent structures for discriminant analysis (OPLS-DA), a supervised, class-based method where class membership is assigned to samples and used to elicit maximum data separation. Visualization of OPLS-DA Scatter plots of the first two-score vectors for each model were drawn based on Hotelling’s multivariate T2, to identify outliers that might bias the results of OPLS-DA. For OPLS-DA, class separation was shown as the first predictive score plotted against the first orthogonal score to visualize the within- and between-class variability associated with the first principal component. S-plots were constructed to identify influential proteins in the separation of breast cancer cell lines. S-plots based on the first principal component show reliability (modeled correlation) plotted against feature magnitude (loadings or modeled covariance). If proteins have variation in correlation and covariance between classes, this plot will assume an S-shape (giving the plot its name), with heavily influential features separating from other features at the upper right and lower left tails of the feature cloud within the model space. Only proteins influential in distinguishing among breast cancer cell lines are reported. All analyses were done using SIMCA-P+ v.12.0.1 (Umetrics, Umea, Sweden).