Signal Alignment Enables Analysis of DIA Proteomics Data from Multisite Experiments
Bibliographic record
Abstract
Abstract DIA has become a mainstream method for quantitative proteomics, however consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing the data-acquisition. To produce a highly consistent and quantitatively accurate data matrix, we have developed DIAlignR which uses raw fragment-ion chromatograms for cross-run alignment. Its performance on a gold standard annotated dataset, demonstrates a threefold reduction in the identification error-rate when compared to standard non-aligned DIA results. A similar performance is achieved for a dataset of 229 runs acquired using 11 different LC-MS/MS setups. Finally, the analysis of 949 plasma runs with DIAlignR increased the number of statistically significant proteins by 43% and 62% for insulin resistant (IR) and respiratory viral infection (RVI), respectively compared to prior analysis without it. Hence, DIAlignR fills a gap in analyzing DIA runs acquired in-parallel using different LC-MS/MS instrumentation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".