Perseus plugin “Metis” for metabolic-pathway-centered quantitative multi-omics data analysis for static and time-series experimental designs
Bibliographic record
Abstract
We introduce Metis, a new plugin for the Perseus software aimed at analyzing quantitative multi-omics data based on metabolic pathways. Data from different omics types are connected through reactions of a genome-scale metabolic-pathway reconstruction. Metabolite concentrations connect through the reactants, while transcript, protein, and protein post-translational modification (PTM) data are associated through the enzymes catalyzing the reactions. Supported experimental designs include static comparative studies and time-series data. As an example for the latter, we combine circadian mouse liver multi-omics data and study the contribution of cycles of phosphoproteome and metabolome to enzyme activity regulation. Our analysis resulted in 52 pairs of cycling phosphosites and metabolites connected through a reaction. The time lags between phosphorylation and metabolite peak show non-uniform behavior, indicating a major contribution of phosphorylation in the modulation of enzymatic activity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.015 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".