Patterned Signal Ratio Biases in Mass Spectrometry-Based Quantitative Metabolomics
Bibliographic record
Abstract
Despite the well-known nonlinear response of electrospray ionization (ESI) in mass spectrometry (MS)-based analysis, its complicated response patterns and negative impact on quantitative comparison are still understudied. We showcase in this work that the patterns of nonlinear ESI response are feature-dependent and can cause significant compression or inflation to signal ratios. In particular, our metabolomics study of serial diluted human urine samples showed that over 72% and 16% metabolic features suffered ratio compression and inflation, respectively, whereas only 12% of the signal ratios represent real metabolic concentration ratios. More importantly, these ratio compression and inflation largely exist in the linear response ranges, suggesting that it cannot be resolved by simply diluting the sample solutions to the linear ESI response ranges. Furthermore, we demonstrated that a polynomial regression model that converts MS signals to sample injection amounts can correct the biased ratios and, surprisingly, outperform the linear regression model in both data fitting and data prediction. Therefore, we proposed a metabolic ratio correction (MRC) strategy to minimize signal ratio bias in untargeted metabolomics for accurate quantitative comparison. In brief, by using the data of serial diluted quality control (QC) samples, we applied a cross-validation strategy to determine the best regression model, between linear and polynomial, for each metabolic feature and to convert the measured MS intensities to QC injection amounts for accurate metabolic ratio calculation. Both the studies of human urine samples and a metabolomics application supported that our MRC approach is very efficient in correcting the biased signal ratios. This novel insight of patterned ESI nonlinear response and MRC workflow can significantly benefit the downstream statistical comparison and biological interpretation for untargeted 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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".