Result Detail – Ordination

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Result Information
Result ID
52
Type
Ordination
Status
completed
Dataset ID
711
Created
2026-09-21 02:17:00.624418
Ordination Result – PCA
Method
PCA
Components
3
Rows ordinated
80
Feature columns
gradient, f1, f2, f3, f4, f5, f6, f7, f8
Explained variance
AxisFractionCumulative
Axis 1 0.6759 0.6759
Axis 2 0.1693 0.8452
Axis 3 0.0743 0.9195
Ordination scatter
Figures
Ordination scores (axes 1–2) Download PNG
Ordination scores (axes 1–2)
Scree plot Download PNG
Scree plot
Top feature loadings Download PNG
Top feature loadings
Pathway Analysis (same dataset)
Run Pathway Analysis

Method & Provenance

Method
Ordination — PCA
Method key
pca (result type: ordination, result #52)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#711 gallery — example_ordination.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/8a2cf4821c2941d4856886bb933d8044 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:17:00.624418

Parameters used

ParameterValue
ordination_methodpca
methodpca
feature_colsgradient, f1, f2, f3, f4, f5, f6, f7, f8
n_components3
color_colgradient

Execution trace

Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made. 

from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
X_scaled = StandardScaler().fit_transform(X)
coords = PCA(n_components=3).fit_transform(X_scaled)