TH‐D‐201C‐09: Evaluation of Metabolomics Data Using Univariate and Multivariate Statistical Analysis Techniques
Bibliographic record
Abstract
Purpose: To quantify urinary metabolic changes in experimental animals as they transitioned from healthy to tumor bearing. Univariate and multivariate statistical tests were used to identify patterns of metabolic behaviour that changed in a significant manner. Receiver operator characteristic (ROC) curve analysis was used to evaluate the utility of the tests. Method and Materials: Human GBM cells were grown as xenografts in NIH iii nude mice at 6 weeks of age. Urine samples were collected daily beginning one week prior to cell injections and lasting four weeks after injection. Selected samples were run on an Oxford 800MHz cold probe NMR Spectrometer (Oxford England) utilizing a 1‐D NOESY pulse sequence. The acquired spectra were analyzed by targeted profiling using Chenomx Suite 5.1 (Chenomx Inc Edmonton Canada). The paired t‐test was used to evaluate changes in metabolite concentrations in the pre‐injection population compared to the post‐injection population. The same test was also applied to metabolite ratios. Principal component analysis (PCA) and partial least squares discriminant analysis (PDS‐DA) were used to search for more complicated relationships in the data. Results: Application of the t‐test to the ratios of metabolites revealed 20 ratios that were significant beyond the Bonferroni correction threshold and 152 more ratios were significant under the FDR condition. Of the significant ratios 7 had an AUC greater than 0.9 and 68 had an AUC between 0.8 and 0.9. After correcting for changes found in the control animals 12 and 36 ratios that satisfied the Bonferroni and FDR thresholds respectively. 6 ratios had an AUC exceeding 0.9 and a further 16 having an AUC greater than 0.8. Conclusion: Metabolic profiling of urine samples in animal models demonstrates that this technique has the potential to serve as a screening tool for certain types of cancers.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".