Post-acquisition filtering of salt cluster artefacts for LC-MS based human metabolomic studies
Bibliographic record
Abstract
Liquid chromatography-high resolution mass spectrometry (LC-MS) has emerged as one of the most widely used platforms for untargeted metabolomics due to its unparalleled sensitivity and metabolite coverage. Despite its prevalence of use, the proportion of true metabolites identified in a given experiment compared to background contaminants and ionization-generated artefacts remains poorly understood. Salt clusters are well documented artefacts of electrospray ionization MS, recognized by their characteristically high mass defects (for this work simply generalized as the decimal numbers after the nominal mass). Exploiting this property, we developed a method to identify and remove salt clusters from LC-MS-based human metabolomics data using mass defect filtering. By comparing the complete set of endogenous metabolites in the human metabolome database to actual plasma, urine and stool samples, we demonstrate that up to 28.5 % of detected features are likely salt clusters. These clusters occur irrespective of ionization mode, column type, sweep gas and sample type, but can be easily removed post-acquisition using a set of R functions presented here. Our mass defect filter removes unwanted noise from LC-MS metabolomics datasets, while retaining true metabolites, and requires only a list of m/z and retention time values. Reducing the number of features prior to statistical analyses will result in more accurate multivariate modeling and differential feature selection, as well as decreased reporting of unknowns that often constitute the largest proportion of human metabolomics data.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".