Ordination Result #53 — Analysis Report
Contents
- pcoa (#53) completed
1. Pcoa
2026-09-21 02:17:05 · ordination
The first two PCOA axes capture 40.2% of the total variance in the data.
| stress | 1571.47598 |
|---|---|
| metric | braycurtis |
| n_components | 3 |
| method | pcoa |
| n_rows | 80 |
| n_dropped | 0 |
| color_col | gradient |
explained variance
| Axis | Fraction | Cumulative |
|---|---|---|
| Axis 1 | 0.284662 | 0.284662 |
| Axis 2 | 0.117255 | 0.401917 |
| Axis 3 | 0.095184 | 0.497101 |
| Axis 4 | 0.084368 | 0.581468 |
| Axis 5 | 0.059372 | 0.64084 |
| Axis 6 | 0.047123 | 0.687963 |
| Axis 7 | 0.042133 | 0.730096 |
| Axis 8 | 0.031862 | 0.761958 |
| Axis 9 | 0.029184 | 0.791142 |
| Axis 10 | 0.023131 | 0.814273 |
ordination scatter
scree
Method & Provenance
- Method
- Ordination — PCoA
- Engine
- Python · scikit-learn 1.8.0
- Input dataset
- gallery — example_ordination.csv
- Started
- 2026-09-21 02:17:05.684714
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/e7a09c01ffc64b94910ff6cb8319a426
| Parameter | Value |
|---|---|
| ordination_method | pcoa |
| method | pcoa |
| feature_cols | gradient, f1, f2, f3, f4, f5, f6, f7, f8 |
| n_components | 3 |
| color_col | gradient |
Command history
# python # Classical MDS on distance matrix # python from app.services.ordination_service import run_ordination # python coords = run_ordination(df, method='pcoa', n_components=3, distance_metric='bray_curtis')