Savitzky–Golay smoothing and differentiation for polymerase chain reaction quantification
Bibliographic record
Abstract
In quantitative PCR (qPCR), replicates can minimize the impact of intra-assay variation; however, inter-assay variations must be minimized to obtain a robust quantification method. The method proposed in this study uses Savitzky–Golay smoothing and differentiation (SGSD) to identify a derivative-maximum-based cycle of quantification. It does not rely on curve modeling, as is the case with many existing techniques. PCR fluorescence data sets challenged for inter-assay variations (different thermocycler units, different reagents batches, different operators, different standard curves, and different labs) were used for the evaluation. The algorithm was compared with a four-parameter logistic model (4PLM) method, the Cy0 method, and the threshold method. The SGSD method compared favourably with all methods in terms of inter-assay variation. SGSD was statistically different from the 4PLM (P = 0.03), Cy0 (P = 0.05), and threshold (P = 0.004) methods on relative error comparison basis. For intra-assay variations, SGSD outperformed the threshold method (P = 0.005) and equalled the 4PLM and Cy0 methods (P > 0.05) on relative error basis. Our results demonstrate that the SGSD method could potentially be an alternative to sigmoid modeling based methods (4PLM and Cy0) when PCR data are challenged for inter-assay variations.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".