A Comprehensive Model for Separating Systematic Bias and Noise in Metabolomic Timecourse Data—A Nonlinear B‐Spline Mixed‐Effects Approach
Bibliographic record
Abstract
The simultaneous detection of tens to hundreds of metabolites in a single metabolomic timecourse sample offers a unique but often unrealized opportunity for quantification validation. An individual timecourse fit for each metabolite fundamentally convolutes measurement noise with systematic sample bias (stemming from, for example, variable sample dilution, extraction, and normalization). However, since systematic bias, by its definition, influences all metabolites within a sample in a similar fashion, it can be identified and corrected through the simultaneous fit of all detected metabolites in a single timecourse model. This study presents a nonlinear B-spline mixed-effects model as a convenient formulation capable of estimating and correcting such bias. The proposed model was successfully applied to real cell culture data and validated using simulated timecourse data perturbed with varying degrees of random noise and systematic bias. The model was able to accurately correct systematic bias of 3%-10% to within 0.5% on average for typical data. An R package for the correction model has been developed to facilitate model adoption and use. The proposed nonlinear B-spline mixed-effects formulation is general enough for application to a broad range of research areas beyond just cell culture metabolomics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".