Table 3.
Illustrative example of merging strategies with imputed values highlighted in italic. Three small datasets (S1, S2, S3) with partially overlapping metabolites (M1–M4) are shown. Top Union-based merging fills missing values with zeros, resulting in a sparse matrix. Center Iterative Similarity-based Merging merges S1 and S2 based on Jaccard similarity and imputes missing values using column-wise means; S3 is treated separately. Bottom Model-guided Agglomerative Merging fills missing values using row-wise means (simulating model-based predictions), producing a more balanced matrix
| Dataset | M1 | M2 | M3 | M4 |
|---|---|---|---|---|
| Union-based merging | ||||
| S1 | 1.20 | 0.80 | 0 | 0 |
| S2 | 0 | 0.90 | 1.50 | 0 |
| S3 | 1.10 | 0 | 0 | 0.70 |
| Iterative similarity-based merging | ||||
| S1 | 1.20 | 0.80 | 1.50 | 0.70 |
| S2 | 1.20 | 0.90 | 1.50 | – |
| S3 | 1.10 | 0.80 | – | 0.70 |
| Model-guided agglomerative merging | ||||
| S1 | 1.20 | 0.80 | 1.40 | 0.60 |
| S2 | 1.15 | 0.90 | 1.50 | 0.65 |
| S3 | 1.10 | 0.85 | 1.45 | 0.70 |