MAFFIN: metabolomics sample normalization using maximal density fold change with high-quality metabolic features and corrected signal intensities
Bibliographic record
Abstract
MOTIVATION: Post-acquisition sample normalization is a critical step in comparative metabolomics to remove the variation introduced by sample amount or concentration difference. Previously reported approaches are either specific to one sample type or built on strong assumptions on data structure, which are limited to certain levels. This encouraged us to develop MAFFIN, an accurate and robust post-acquisition sample normalization workflow that works universally for metabolomics data collected on mass spectrometry (MS) platforms. RESULTS: MAFFIN calculates normalization factors using maximal density fold change (MDFC) computed by a kernel density-based approach. Using both simulated data and 20 metabolomics datasets, we showcased that MDFC outperforms four commonly used normalization methods in terms of reducing the intragroup variation among samples. Two essential steps, overlooked in conventional methods, were also examined and incorporated into MAFFIN. (i) MAFFIN uses multiple orthogonal criteria to select high-quality features for normalization factor calculation, which minimizes the bias caused by abiotic features or metabolites with poor quantitative performance. (ii) MAFFIN corrects the MS signal intensities of high-quality features using serial quality control samples, which guarantees the accuracy of fold change calculations. MAFFIN was applied to a human saliva metabolomics study and led to better data separation in principal component analysis and more confirmed significantly altered metabolites. AVAILABILITY AND IMPLEMENTATION: The MAFFIN algorithm was implemented in an R package named MAFFIN. Package installation, user instruction and demo data are available at https://github.com/HuanLab/MAFFIN. Other data in this work are available on request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".