How Can S-Plots Be Used to Select Variables with p < 0.05 in PLS-DA/OPLS-DA Analysis?
When applying S-plots to identify variables with statistically significant differences (p < 0.05) from PLS-DA (Partial Least Squares Discriminant Analysis) or OPLS-DA (Orthogonal PLS-DA) models, the following workflow is recommended:
Model Construction
Begin by conducting PLS-DA or OPLS-DA modeling using an appropriate statistical software package or computational environment, such as R, MATLAB, or Python.
S-Plot Generation
Based on the established model, generate an S-plot using the available analytical tools. This plot visualizes each variable's covariance and correlation with the model components, aiding in the identification of variables with strong discriminative potential.
Identification of Key Variables
Inspect the S-plot for points located farthest from the origin. These points typically represent variables with the greatest influence on group separation and are considered candidates for further statistical evaluation.
Statistical Testing
Apply appropriate univariate statistical tests (e.g., t-tests or ANOVA) to the identified variables to determine their statistical significance in terms of p-values.
Selection Based on p < 0.05
Filter the results to retain only those variables with p-values less than 0.05, indicating statistically significant contributions to group differentiation.
Validation of Selected Variables
To ensure the robustness and reproducibility of the selected variables, perform cross-validation or validate findings using an independent dataset.
By following this systematic approach, statistically significant variables can be reliably extracted from PLS-DA or OPLS-DA models, potentially contributing to biomarker discovery or other biomedical research applications.
MtoZ Biolabs, an integrated chromatography and mass spectrometry (MS) services provider.
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