Result Detail – Ordination

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Result Information
Result ID
53
Type
Ordination
Status
completed
Dataset ID
711
Created
2026-09-21 02:17:05.684714
Ordination Result – PCOA
Method
PCOA
Components
3
Stress
1571.4760
Distance metric
braycurtis
Rows ordinated
80
Feature columns
gradient, f1, f2, f3, f4, f5, f6, f7, f8
Explained variance
AxisFractionCumulative
Axis 1 0.2847 0.2847
Axis 2 0.1173 0.4019
Axis 3 0.0952 0.4971
Axis 4 0.0844 0.5815
Axis 5 0.0594 0.6408
Axis 6 0.0471 0.6880
Axis 7 0.0421 0.7301
Axis 8 0.0319 0.7620
Axis 9 0.0292 0.7911
Axis 10 0.0231 0.8143
Ordination scatter
Figures
Ordination scores (axes 1–2) Download PNG
Ordination scores (axes 1–2)
Scree plot Download PNG
Scree plot
Pathway Analysis (same dataset)
Run Pathway Analysis

Method & Provenance

Method
Ordination — PCoA
Method key
pcoa (result type: ordination, result #53)
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/e7a09c01ffc64b94910ff6cb8319a426 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:17:05.684714

Parameters used

ParameterValue
ordination_methodpcoa
methodpcoa
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. 

# Classical MDS on distance matrix
from app.services.ordination_service import run_ordination
coords = run_ordination(df, method='pcoa', n_components=3, distance_metric='bray_curtis')