Pipeline type: PCA
PCA Summary
Loadings
| Feature | PC1 | PC2 | PC3 |
|---|---|---|---|
| layer_1__Protein_content | 0.0134 | 0.4793 | -0.0309 |
| layer_1__Alanine | -0.0330 | 0.1766 | 0.4614 |
| layer_1__Glycine | 0.0109 | 0.1379 | -0.1891 |
| layer_1__Serine | 0.0196 | -0.2037 | 0.3561 |
| layer_1__Threonine | -0.3105 | 0.0300 | -0.0298 |
| layer_1__Valine | -0.1690 | 0.3253 | 0.0018 |
| layer_1__Leucine | 0.1662 | -0.3456 | 0.1100 |
| layer_1__Isoleucine | 0.1579 | -0.0670 | -0.1872 |
| layer_1__Proline | 0.1927 | -0.0930 | -0.2010 |
| layer_1__Phenylalanine | 0.0186 | -0.1385 | 0.1344 |
| layer_1__Tryptophan | 0.0434 | -0.2062 | -0.2830 |
| layer_2__Protein_content | -0.3042 | 0.1150 | 0.0999 |
| layer_2__Protein_A01 | -0.1329 | -0.2582 | -0.0149 |
| layer_2__Protein_B02 | 0.0444 | -0.0023 | -0.3378 |
| layer_2__Protein_C03 | 0.4014 | 0.1766 | -0.0556 |
| layer_2__Protein_D04 | 0.0616 | 0.0206 | 0.4232 |
| layer_2__Protein_E05 | 0.3059 | -0.2226 | 0.0592 |
| layer_2__Protein_F06 | 0.1044 | -0.0779 | -0.0981 |
| layer_2__Protein_G07 | 0.3734 | 0.0672 | 0.0263 |
| layer_2__Protein_H08 | -0.2575 | -0.1686 | -0.2871 |
| layer_2__Protein_I09 | 0.4044 | 0.0678 | 0.0929 |
| layer_2__Protein_J10 | -0.1746 | -0.4158 | 0.1602 |
Figures
Method & Provenance
- Method
- Multi-omics — PCA
- Method key
pca(result type:pca, result #87)- Engine
- Python · scikit-learn (PCA) v1.8.0
- Dataset(s)
- #720 gallery — example_metabolomics.csv
- Status
- completed
- Permanent link
- https://cat-g-omxpert.wishartlab.com/multiomics/results/by-token/cf33b3a5a2614cd2b556dd9f7a646a10 Bookmark or share this URL — it does not depend on your session.
- Created
- 2026-09-21 02:18:19.384288
Parameters used
| Parameter | Value |
|---|---|
dataset_ids | — |
pipeline_type | pca |
Execution trace
Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made.
from sklearn.decomposition import PCAcoords = PCA(n_components=2).fit_transform(X)