Unsupervised Data Analysis Methods used in Qualitative and Quantitative Metabolomics and Metabonomics
Bibliographic record
Abstract
Metabolomics or metababonomics is one of the major high throughput analysis methods that endeavors holistic measurement of metabolic profiles of biological systems. Data analysis approaches in metabolomics can broadly be divided into qualitative – analysis of spectral data and quantitative – analysis of individual metabolite concentrations. In this work, the author will demonstrate the benefits and limitations of different unsupervised analysis tools currently utilized in qualitative and quantitative metabolomics data analysis. Following a detailed literature review outlining different applications of unsupervised methods in metabolomics, the author shows examples of an application of the major previously utilized unsupervised analysis methods. The testing of these methods was performed using qualitative as well as corresponding quantitative metabolite data derived to represent a large set of 2,000 objects. Spectra of mixtures were obtained from different combinations of experimental NMR measurements of 13 prevalent metabolites at five different groups of concentrations representing different phenotypes. The analysis shows advantages and disadvantages of standard tools when applied specifically to metabolomics.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".