Guidism
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1) Data
Upload CSV (first two columns:
Sample
,
Condition
)
Or paste CSV/table
Detected columns
—
Sample column
Condition column
Transform
None
Arcsin √ (percentages)
Scaling
Z-score (recommended)
None
Missing values
Impute with column mean
Drop rows with any missing
Impute with 0
Point labels
Hide
Show sample names
Run PCA
Download Scores CSV
Download Loadings CSV
Load data to see schema…
2) Results
Scatter components
Ellipse confidence
68% confidence
95% confidence
99% confidence
Outliers & Trimming
Mode
Global (ignore condition)
Per condition
Metric
Mahalanobis D² (within group)
Centroid Δ with pooled covariance
Silhouette (Euclidean)
PCs for outlier scoring (K)
Regularization ε
Compute outlier scores
Download scores CSV
Remove top n
Scope
Global: top n overall
Per condition: top n per condition
Use selected rows
Preview Trim
Apply Trim
Reset
Download trim history CSV
No preview.
No trims applied yet.
3) Plots
Copy
Download
Show point labels
Copy
Download
4) Top Loadings
Feature
PC
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