Establishing ion ratio thresholds based on absolute peak area for absolute protein quantification using protein cleavage isotope dilution mass spectrometry
Bibliographic record
Abstract
Quantitative mass spectrometry has become central to the field of proteomics and metabolomics. Selected reaction monitoring is a widely used method for the absolute quantification of proteins and metabolites. This method renders high specificity using several product ions measured simultaneously. With growing interest in quantification of molecular species in complex biological samples, confident identification and quantitation has been of particular concern. A method to confirm purity or contamination of product ion spectra has become necessary for achieving accurate and precise quantification. Ion abundance ratio assessments were introduced to alleviate some of these issues. Ion abundance ratios are based on the consistent relative abundance (RA) of specific product ions with respect to the total abundance of all product ions. To date, no standardized method of implementing ion abundance ratios has been established. Thresholds by which product ion contamination is confirmed vary widely and are often arbitrary. This study sought to establish criteria by which the relative abundance of product ions can be evaluated in an absolute quantification experiment. These findings suggest that evaluation of the absolute ion abundance for any given transition is necessary in order to effectively implement RA thresholds. Overall, the variation of the RA value was observed to be relatively constant beyond an absolute threshold ion abundance. Finally, these RA values were observed to fluctuate significantly over a 3 year period, suggesting that these values should be assessed as close as possible to the time at which data is collected for quantification.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".