The State of RT-Quantitative PCR: Firsthand Observations of Implementation of Minimum Information for the Publication of Quantitative Real-Time PCR Experiments (MIQE)
Bibliographic record
Abstract
In the past decade, the techniques of quantitative PCR (qPCR) and reverse transcription (RT)-qPCR have become accessible to virtually all research labs, producing valuable data for peer-reviewed publications and supporting exciting research conclusions. However, the experimental design and validation processes applied to the associated projects are the result of historical biases adopted by individual labs that have evolved and changed since the inception of the techniques and associated technologies. This has resulted in wide variability in the quality, reproducibility and interpretability of published data as a direct result of how each lab has designed their RT-qPCR experiments. The 'minimum information for the publication of quantitative real-time PCR experiments' (MIQE) was published to provide the scientific community with a consistent workflow and key considerations to perform qPCR experiments. We use specific examples to highlight the serious negative ramifications for data quality when the MIQE guidelines are not applied and include a summary of good and poor practices for RT-qPCR.
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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 | 0.395 | 0.478 |
| Meta-epidemiology (narrow) | 0.001 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.025 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".