How Should One Interpret an OPLS-DA Plot in MetaboAnalyst?
An OPLS-DA plot typically comprises a score plot and a loading plot. The score plot illustrates how samples are projected onto the principal components of the model and is commonly utilized to depict the separation between different groups, such as disease and control groups. The loading plot, on the other hand, indicates the contribution of individual variables (e.g., metabolites) in distinguishing between these groups.
Interpretation of the Score Plot
1. Sample Clustering
Assess whether samples from different groups form distinct clusters. A well-constructed model should exhibit clear separation between groups.
2. Model Performance Indicators
Evaluate the R² (explained variance) and Q² (predictive power) values. An R² value close to 1 suggests a strong model fit, whereas a Q² value close to 1 indicates robust predictive ability.
Interpretation of the Loading Plot
1. Identifying Key Variables
Variables that are farther from the origin in the loading plot contribute more substantially to inter-group differentiation. These variables may represent key metabolites distinguishing different sample groups.
2. Understanding Variable Directionality
The spatial distribution of variables in the loading plot reflects their relationship with group separation. For instance, in a disease vs. control comparison, metabolites positioned toward the disease group may be upregulated under pathological conditions.
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