Result Detail – Ordination

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Result Information
Result ID
55
Type
Ordination
Status
completed
Dataset ID
720
Created
2026-09-21 02:18:03.435062
Ordination Result – PCA
Method
PCA
Components
3
Rows ordinated
20
Feature columns
Protein_content, Alanine, Glycine, Serine, Threonine, Valine, Leucine, Isoleucine, Proline, Phenylalanine, Tryptophan
Explained variance
AxisFractionCumulative
Axis 1 0.2138 0.2138
Axis 2 0.1855 0.3993
Axis 3 0.1669 0.5662
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 #55)
Engine
Python · scikit-learn v1.8.0
Dataset(s)
#720 gallery — example_metabolomics.csv
Status
completed
Permanent link
https://cat-g-omxpert.wishartlab.com/results/by-token/f39c0418559c409bb880f45913bcdc31 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:18:03.435062

Parameters used

ParameterValue
ordination_methodpca
methodpca
feature_colsProtein_content, Alanine, Glycine, Serine, Threonine, Valine, Leucine, Isoleucine, Proline, Phenylalanine, Tryptophan
n_components3
color_colGroup

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)