S-Plot Marker Plot
🎯 What is this?
S-Plot is the "killer" chart of the OPLS family (OPLS / OPLS-DA), specially used to screen markers (Biomarker / Marker).
It answers two questions at the same time:
- Is this variable's contribution large?
- Is this variable trustworthy?
An ordinary correlation analysis can only answer one of them. S-Plot draws both on the same chart, so variables that are "both important and reliable" automatically gather in two corners.
🧐 How to read?
| Axis | Meaning |
|---|---|
| X-axis (p1) | Covariance——the variable's contribution to the model |
| Y-axis (p(corr)) | Correlation coefficient——the reliability of the correlation between the variable and the result |
Meaning of the four regions:
| Position | Meaning |
|---|---|
| Top right corner ⭐ | Large contribution and stable positive correlation → positive marker candidate |
| Bottom left corner ⭐ | Large contribution and stable negative correlation → negative marker candidate |
| Middle right | Large contribution but unstable correlation → suspicious result, use with caution |
| Near the origin | Small contribution → a variable that can be removed |
💡 One-line memory aid: the closer to the corner, the more reliable. True markers "swing" out to the top-right or bottom-left corners, forming an obvious S shape (which is also where the name S-Plot comes from).
🛠️ How to use?
1. Quickly lock onto key variables
Use the lasso to circle the top-right / bottom-left region; these variables are the marker candidates to focus on first.
2. Simplify the model
Remove the variables near the origin and rebuild the model; this usually makes the model more concise with better generalization.
3. Cross-validate with VIP
| Chart | Focus |
|---|---|
| VIP Plot | Only "contribution size" (one-dimensional ranking) |
| S-Plot | Both "contribution + reliability" (two-dimensional) |
💡 Best practice: take the intersection of the two——variables that are both on the VIP > 1 list and fall in the corners of the S-Plot are the truly reliable markers.
⚠️ Applicable models
S-Plot is available only for the following models:
- OPLS / OPLS-DA (recommended; after orthogonal correction the covariance is cleaner)
- PLS / PLS-DA (usable, but lacking orthogonal noise stripping, the pattern may be more divergent)
⚠️ PCA models have no Y, so covariance and correlation cannot be computed, and S-Plot is not supported.
🎯 Typical applications
- Authenticity identification: find the characteristic wavelengths that distinguish genuine products from imitations in spectral data
- Raw material grading: locate the key physicochemical indicators that determine the grade
- Metabolomics: screen differential metabolites
- Process diagnosis: find the key parameters that distinguish "normal batches" from "abnormal batches"
💡 After finding candidate markers, it is recommended to use Causal Inference (DML) to further verify the causal relationship, and avoid mistaking "accompanying changes" for "adjustable levers".