How to Construct an S-plot in OPLS-DA?
S-plot is a widely used visualization tool in OPLS-DA or PLS-DA for identifying variables that contribute most to class separation. By integrating loadings and correlation coefficients, S-plot presents variables in a structured and interpretable manner. The following are the fundamental steps for constructing an S-plot analysis:
Data Preparation
A dataset previously analyzed using OPLS-DA is required. This dataset should include measured values of relevant variables, such as metabolite concentrations or gene expression levels.
Running the OPLS-DA Model
Perform OPLS-DA on the dataset to develop a model that differentiates experimental groups or conditions. This process typically involves calculating scores, loadings, and validating the model through cross-validation and prediction accuracy assessment.
Extracting Model Parameters
Retrieve key parameters from the OPLS-DA model, particularly loadings (p) and correlation coefficients (p(corr)). Loadings indicate the extent to which each variable contributes to the model, while correlation coefficients describe the relationship between variables and the response variable.
Generating the S-plot
Construct the S-plot using the extracted loadings and correlation coefficients. In the S-plot, the x-axis represents loadings (p), while the y-axis represents standardized correlation coefficients (p(corr)). This graphical representation facilitates the identification of variables that significantly differentiate classes, such as distinguishing disease from healthy states.
Interpreting the Results
Variables positioned at the periphery of the plot (i.e., far from the origin) are considered most influential, as they exhibit strong signals relevant to class separation. A high loading value signifies a substantial contribution to the model, whereas a high correlation coefficient indicates a strong association with the response variable.
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