Comparison of accuracy and precision between multipoint calibration, single point calibration, and relative quantification for targeted metabolomic analysis
Bibliographic record
Abstract
Targeted metabolomics requires accurate and precise quantification of candidate biomarkers, often through tandem mass spectrometric (MS/MS) analysis. Differential isotope labeling (DIL) improves mass spectrometric (MS) analysis in metabolomics by derivatizing metabolites with two isotopic forms of the same reagent. Despite its advantages, DIL-liquid chromatographic (LC)-MS/MS can result in substantial increase in workload when fully validated quantitative methods are required. To decrease the workload, we hypothesized that single point calibration or relative quantification could be used as alternative methods. Either approach will result in significant saving in resources and time. To test our hypothesis, six urinary metabolites were selected as model compounds. Urine samples were analyzed using a fully validated multipoint dansyl chloride-DIL-LC-MS/MS method. Samples were reprocessed using single point calibration and relative quantification modes. Our results demonstrated that the performance of single point calibration or relative quantification was inferior, for some metabolites, to multipoint calibration. The lower limit of quantification failed in the quantification of ethanolamine in most of participant samples using single point calibration. In addition, its precision was not acceptable in one participant during serine and ethanolamine quantification. On the other hand, relative quantification resulted in the least accurate data. In fact, none of the data generated from relative quantification for serine was comparable to that obtained from multipoint calibration. Finally, while single point calibration showed an overall acceptable performance for the majority of the model compounds, we cannot extrapolate the findings to other metabolites within the same analytical run. Analysts are advised to assess accuracy and precision for each metabolite in which single point calibration is the intended quantification mean.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".