Systematic Assessment of the Reproducibility of Relative Quantification Based on LC-MS with Replicates
Bibliographic record
Abstract
RP-106 To develop a method to assess and display reproducibility of protein quantification based on LC-MS with replicates. The reproducibility of relative quantification based on LC-MS was assessed uniquely at three levels: Data level - Peptide features from each of the replicates were detected and aligned between the replicates. The data reproducibility was analyzed by paired T-test and correlation analysis on the intensities of paired features. Venn diagrams of peptide features between replicates were used to display the reproducibility. Identification level – Protein identification was conducted on each replicate. The reproducibility of the identified peptides and proteins between replicates was assessed by paired T-test and correlation analysis. The boxplot diagram was used to represents the distribution of the logarithmic ratios of identified feature pairs. Quantification level - The relative peptide/protein abundance was computed based on the intensity of each peptide ion. The reproducibility of peptide and protein quantification was assessed by paired T-test and ANOVA. This method was implemented in the PEAKSTM software suite and tested by a standard protein mixture UPS2 with two replicate experiments. The number of peptide features in replicate 1 and 2 are 2994 and 2934. The number of common features is 2794. The Pearson correlation value is 85% for common features. The total number of identified feature pairs in replicate 1 and 2 is 518. The corresponding Pearson correlation value is 92%. 14 out of 16 abundant proteins were reported in both replicate 1 and 2. 81 of 518 unique feature pairs were used for protein quantification. The corresponding Pearson correlation value is 94%. 12 of 14 identified proteins were quantified. Mean of ratios was 0.92, and variance was 0.013. This method could efficiently assess and display the reproducibility at three levels, which helps to evaluate both the LC-MS experiment and the quantification algorithm.
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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 | 0.040 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".