Review Reports - Quantification Revisited: What qPCR Efficiency Models Reveal About Data Analysis Integrity

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript "Quantification Revisited: What qPCR Efficiency Models Reveal About Data Analysis Integrity" provides a timely and critically important overview of the current state of quantitative PCR. The authors, recognized leaders in the field, deliver a clear, well-structured paper. The compelling illustrations (particularly Figures 1 and 5) effectively demonstrate the catastrophic consequences of ignoring amplification efficiency.

While the analysis of classical models (LinRegPCR, sigmoidal models) is brilliant, the article could place more emphasis on emerging approaches based on machine learning (ML) and artificial intelligence. In 2024–2025, significant publications have emerged regarding the use of XGBoost and deep neural networks for the automated analysis of qPCR and dPCR curves. It is recommended to add a section discussing how ML might address the "subjectivity" of threshold and baseline settings mentioned in the text.

In the section regarding low-copy samples, the authors mention dPCR. It would be beneficial to provide a more detailed comparison of efficiency modeling requirements between qPCR and dPCR. Since dPCR is inherently less dependent on efficiency, this comparison would further underscore the necessity of rigorous modeling specifically for qPCR.

In Table 1, adding a column with examples of software implementing each method (e.g., LinRegPCR, qBase, REST, FastFinder) would greatly enhance the practical value for the reader.Regarding the "Collective Blind Spot" (Section 6), the authors could provide more concrete examples of how editorial policies of specific journals or publishing groups have changed (or failed to change) since the original MIQE guidelines were published.

The section dedicated to the sociology of the scientific community deserves special mention. The authors introduce the term "Collective Blind Spot" to describe a situation where all participants (authors, reviewers, and editors) tacitly agree to ignore methodological requirements in favor of publication speed and high-impact results. The authors propose a mandatory three-question checklist for editors:

1 Are efficiencies reported and plausible for all assays?

2 Have efficiency values been incorporated into the calculation of results?

3 Has uncertainty (confidence intervals) been propagated through the final calculations?

If the answer to any of these is "no," the quantitative claim of the paper should not be supported. I fully agree with the authors on this point.

The article allows for the following conclusions for the scientific community:

1 Amplification efficiency must be measured and reported for each individual reaction or group of homogeneous samples, rather than assuming 100%.

2 Using quantification cycle (Cq) values without efficiency correction leads to systematic biases that can distort biological conclusions.

3 Scientific journals must move from formal MIQE citations to actual enforcement of methodological parameters through mandatory checklists.

4 The future of the field lies in integrating classical kinetics with machine learning models and transitioning toward absolute quantification.

Author Response

Reviewer 1

The manuscript “Quantification Revisited: What qPCR Efficiency Models Reveal About Data Analysis Integrity” provides a timely and critically important overview of the current state of quantitative PCR. The authors, recognized leaders in the field, deliver a clear, well-structured paper. The compelling illustrations (particularly Figures 1 and 5) effectively demonstrate the catastrophic consequences of ignoring amplification efficiency.

While the analysis of classical models (LinRegPCR, sigmoidal models) is brilliant, the article could place more emphasis on emerging approaches based on machine learning (ML) and artificial intelligence. In 2024–2025, significant publications have emerged regarding the use of XGBoost and deep neural networks for the automated analysis of qPCR and dPCR curves. It is recommended to add a section discussing how ML might address the “subjectivity” of threshold and baseline settings mentioned in the text.

We have added a section discussing recent machine learning approaches to amplification curve analysis. The revised text acknowledges that such methods may reduce operator-dependent variability in baseline and threshold selection and improve consistency of curve interpretation. However, we also clarify that these approaches do not remove the need for explicit estimation and incorporation of amplification efficiency within the quantitative framework of qPCR.

We have revised and expanded the section on dPCR. The revised text clarifies that although dPCR does not incorporate efficiency into a kinetic quantification equation in the same way as qPCR, amplification efficiency remains relevant, as it determines whether a template present in a partition produces a detectable endpoint signal. We therefore distinguish between the central role of efficiency in the qPCR quantification model and its different, but still important, role in dPCR.

In Table 1, adding a column with examples of software implementing each method (e.g., LinRegPCR, qBase, REST, FastFinder) would greatly enhance the practical value for the reader.

We have incorporated examples of commonly used software implementations into the Table 1 legend. Adding a separate column made the table structurally unwieldy and reduced readability; inclusion in the legend preserves clarity while addressing the reviewer’s suggestion.

Regarding the “Collective Blind Spot” (Section 6), the authors could provide more concrete examples of how editorial policies of specific journals or publishing groups have changed (or failed to change) since the original MIQE guidelines were published.

We agree that the evolution of editorial policy since the introduction of the original MIQE guidelines is an important issue. However, we consider that naming specific journals or publishing groups, particularly in terms of perceived successes or shortcomings in implementation, would require a systematic and objective survey beyond the scope of the present review. Our aim in this section is to describe a structural issue affecting the field as a whole rather than to single out individual journals. We have therefore retained the discussion at a community-wide level while strengthening the text to emphasise the broader pattern of variable MIQE enforcement and the need for more consistent editorial scrutiny.

The authors propose a mandatory three-question checklist for editors:

1 Are efficiencies reported and plausible for all assays?

2 Have efficiency values been incorporated into the calculation of results?

3 Has uncertainty (confidence intervals) been propagated through the final calculations?

If the answer to any of these is “no,” the quantitative claim of the paper should not be supported. I fully agree with the authors on this point.

We appreciate the reviewer’s agreement with this proposal. The checklist remains unchanged.

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript, "Quantification Revisited: What qPCR Efficiency Models Reveal About Data Analysis Integrity," is not only a profound revisit of qPCR theory but also a timely reminder of the critical importance of scientific integrity and data rigor in contemporary research. The logic of the manuscript is impeccable, seamlessly integrating complex mathematical models with the underlying biochemical essence of the reactions. It holds exceptional academic value and provides vital pedagogical guidance for the field. Given the high quality of the work, I recommend a Minor Revision. My suggestions below are intended to enhance the practical utility of the review, particularly for frontline and early-career researchers who often operate within the "black box" of commercial software, rather than to question the manuscript’s solid theoretical foundation.

Some Suggestions for Revision:

Defining New Standards for Efficiency (Revisiting the 90–110% Rule): The introduction correctly critiques the widely accepted 90–110% efficiency range, illustrating the substantial errors this can introduce after 30 cycles. While debunking old standards is essential, the authors should propose more definitive guidelines. For instance, if an efficiency of 85% or 115% is measured, is the data considered usable provided a Pfaffl-style correction is applied? Or does such a significant deviation from 100% indicate an inherently unreliable assay that should be discarded regardless of mathematical correction? Clarifying the boundary between "mathematically correctable" and "biochemically robust" would be highly beneficial.

Quantitative Guidance on Poisson Error Replicates: In Figure 4 and the associated discussion regarding Poisson distribution errors at low copy numbers, the manuscript suggests "increasing technical replicates" or switching to dPCR. However, since dPCR remains cost-prohibitive for many laboratories, the advice to simply "increase replicates" lacks specificity. Could the authors provide more concrete, model-based recommendations? For example, when Cq > 30 or the copy number is < 100, should the standard triplicate approach be increased to five or more replicates?

Addressing the "Black Box" Barrier of Commercial Software: The manuscript offers a sharp critique of the reliance on automated thresholds and baseline settings in instrument-specific software, which renders data a "black box" product. This is a significant pain point; however, many researchers find exporting raw fluorescence data for third-party analysis (e.g., LinRegPCR) challenging due to proprietary formats and the lack of standardized protocols. I suggest adding a paragraph or a dedicated "Box" providing brief, practical guidance on how to extract raw data from major platforms (e.g., ABI, Roche) to facilitate more transparent analysis.

Defining Requirements for a Standard Curve: While the authors acknowledge the transparency of the standard curve method, they also note its limitations. In light of the impending MIQE 2.0 updates, I suggest explicitly defining the minimum requirements for standard curve. Many published studies rely on curves with only three dilution points or fail to cover the dynamic range of their actual samples. To ensure a reliable efficiency estimate, what is the recommended minimum number of dilution points and replicates per point?

Technical Formatting of Table 1: Please ensure that Table 1 adheres to the standard academic journal formatting (e.g., using professional three-line table style where appropriate). Please check that the row and column alignments are consistent with the journal’s style guide to ensure readability and professional presentation.

Contextualizing the Extreme Example in Figure 5: The case study in Figure 5 is highly impactful but presents a somewhat extreme scenario (Target efficiency 72% vs. Reference efficiency 91%). This might lead some readers to believe that efficiency correction is only necessary when results are as poor as 72%. I suggest adding a brief discussion or a supplementary example to show that even subtle efficiency differences can lead to statistically significant false positives or negatives after calculation. This would better demonstrate the universal necessity of efficiency correction, rather than framing it as a solution only for failed assays.

Author Response

Reviewer 2

The manuscript, “Quantification Revisited: What qPCR Efficiency Models Reveal About Data Analysis Integrity,” is not only a profound revisit of qPCR theory but also a timely reminder of the critical importance of scientific integrity and data rigor in contemporary research. The logic of the manuscript is impeccable, seamlessly integrating complex mathematical models with the underlying biochemical essence of the reactions. It holds exceptional academic value and provides vital pedagogical guidance for the field. Given the high quality of the work, I recommend a Minor Revision.

Defining New Standards for Efficiency (Revisiting the 90–110% Rule)

We have revised the manuscript to address this point directly. The revised text distinguishes between mathematical correctability and biochemical robustness. We clarify that efficiency-corrected models are mathematically defined for positive efficiency values, but that efficiencies exceeding 100% introduce systematic bias because they cannot correspond to the reaction that generated the observed Cq values. We therefore state explicitly that efficiencies above 100% should not be used in correction models and should instead be treated as indicators of technical artefact requiring assay optimisation. Rather than defining a new acceptable range, we propose that the reliability of an assay should be judged in terms of stability, reproducibility, and explicit propagation of uncertainty.

Quantitative Guidance on Poisson Error Replicates

We have expanded the relevant section to clarify that the appropriate number of technical replicates cannot be defined by a fixed Cq threshold alone, as the relationship between Cq and starting copy number is assay-dependent. We emphasise that replication should be guided by the precision required for the biological question and that uncertainty from sampling variation and efficiency estimation should be propagated to determine whether the resulting confidence intervals are compatible with the level of discrimination required between groups. This reframes replication as a statistical design choice rather than a fixed convention.

Addressing the “Black Box” Barrier of Commercial Software

We agree that access to raw fluorescence data is essential for transparent analysis and that proprietary formats can present practical barriers. However, providing platform-specific procedural guidance would extend beyond the scope of this conceptual and analytical review and would rapidly become outdated. We have therefore retained the discussion at a general level, emphasising the importance of raw data access and transparent reporting without introducing protocol-level instructions tied to specific commercial systems.

Defining Requirements for a Standard Curve

We do not consider it appropriate to prescribe a universal minimum number of concentrations and replicates. Instead, we emphasise principles that determine reliability, including coverage of the relevant concentration range, independence of standards, regression stability, and associated confidence intervals. We have revised the manuscript to make this principle-based approach explicit.

Technical Formatting of Table 1

Table formatting in the submitted manuscript follows the journal’s template. We will ensure that the table adheres fully to the journal’s formatting requirements at the proof stage, including alignment and styling consistent with the publisher’s guidelines.

Reviewer 3 Report

Comments and Suggestions for Authors

The author has conducted a detailed analysis in the review on the role of amplification efficiency in qPCR quantification, the consequences of ignoring, assuming, or misusing efficiency, and the practical impact on data interpretation. Here are my comments:

The author has provided extensive analysis on the role of amplification efficiency in qPCR quantification, but no specific discussion section is included. It is recommended that the author add a chapter for discussion;
The author could also attempt to discuss what steps should be taken to minimize errors when amplification efficiency issues arise;
Should Table 1 be treated as an image and changed to Figure 2, with the font enlarged a bit?

Author Response

Reviewer 3

The author has provided extensive analysis on the role of amplification efficiency in qPCR quantification, but no specific discussion section is included. It is recommended that the author add a chapter for discussion;

The manuscript is intentionally structured as an integrated analytical review rather than following a conventional IMRaD format. Interpretive discussion is embedded throughout the text, particularly in the sections addressing efficiency modelling, error propagation, standard curve design, Poisson sampling effects, and the broader implications for data interpretation and editorial practice. For this reason, a separate “Discussion” section has not been added, as the analytical and interpretive components are already integrated within each thematic section.

The author could also attempt to discuss what steps should be taken to minimize errors when amplification efficiency issues arise;

This issue is addressed across multiple sections of the revised manuscript. The text now explicitly outlines practical considerations for recognising and managing efficiency-related error, including assessment of the stability and reproducibility of efficiency estimates, appropriate use of efficiency-corrected models, propagation of uncertainty into final quantitative results, careful standard curve design, and increased replication where stochastic sampling dominates. Together, these elements provide a structured framework for minimising quantitative bias without reducing the review to prescriptive protocol-level instruction.

Should Table 1 be treated as an image and changed to Figure 2, with the font enlarged a bit?

Table 1 is intended as a comparative summary of analytical approaches and is best retained in tabular form to preserve clarity and facilitate comparison across methods. Converting it to a figure would reduce flexibility during production and is unlikely to improve interpretability. Final formatting, including font size, alignment, and styling, will be standardised according to the journal’s typesetting requirements at the proof stage.

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

The author has made the requested revisions and I have no further comments.