Mastering dMIQE Guidelines for the Publication of dPCR Data

This article highlights some of the specific recommendations from the dMIQE guidelines that can help support high-quality digital PCR (dPCR) experiments:

  1. The impact of the number of microreactions on relative uncertainty in dPCR experiments
  2. The importance of equal volume dPCR microreactions
  3. Clear discrimination of signal from positive and negative microreactions

Digital PCR has emerged as a powerful technique for quantifying nucleic acids with high precision and sensitivity. In 2020, a significant update to the Minimum Information for Publication of Quantitative Digital PCR Experiments (dMIQE) guidelines was published in the journal Clinical Chemistry to ensure the credibility and reproducibility of dPCR experiments. What do these guidelines mean and how do they help you achieve more reproducible results?


The 2020 dMIQE guidelines build upon the previous versions and are designed to provide a comprehensive framework for conducting high-quality dPCR experiments. The main goal of these guidelines is to enhance the transparency and accuracy of dPCR data reporting, enabling researchers to evaluate and reproduce results across different laboratories.

The updated guidelines cover crucial aspects of dPCR experiments, such as assay design, sample handling, instrument calibration, data analysis, and quality control. By following these guidelines, researchers can optimize the setup and execution of their experiments and work to minimize potential sources of variability and bias.

Key recommendations in the 2020 dMIQE guidelines include a wide range of topics to help ensure proper assay and probe selection for optimal sensitivity and specificity, handling and preparation recommendations to help minimize contamination and degradation, and recommendations around proper instrument calibration and validation, all with the goal of supporting precise and accurate measurements.

Microreaction math

The Poisson distribution—the statistical principle that governs dPCR

The Poisson distribution model assumes that target molecules are randomly and independently distributed among the microreaction. Poisson distribution becomes a valid approximation of the true distribution of target molecules with the more microreactions per sample that occur (The dMIQE Group, 2020). In this context the "Law of Large Numbers" comes into play, which states that as the number of reactions increases, the observed frequencies (number of positive microchambers with the target) converge to the true probabilities (probability of a reaction containing a target molecule). Having a large number of reactions allows researchers to achieve the following benefits:

  1. Increased statistical confidence: With more microreactions, the likelihood of having more microreactions positive for the target increases. This leads to higher statistical confidence in the quantification of the target concentration.
  2. Better representation of the sample: A larger number of microreactions paired with improved sample volume testing (see digital pooling tech note) can help improve the random sampling of the target molecules in the sample, making the results more representative of the actual target concentration. It can also reduce sampling errors ensuring that the quantification is less affected by outliers.
  3. Enhanced sensitivity and precision: The statistical power of dPCR increases with the number of microreactions, enabling accurate quantification even at low target concentrations

IMPORTANT: Having more microreactions helps bring you “closer to the truth” by helping to minimize the impact of uncertainty.


Furthermore, a figure from the dMIQE guidelines (Figure 1) provides a fascinating insight. Based on modeling the 95% confidence limit of λ, they see drastic improvement of the relative uncertainty experiences at the 10K microreaction point. Beyond this point, while the enhancements in relative uncertainty are still evident, a more gradual trend is seen. Understanding this transition can help researchers optimize their dPCR experiments based on the dynamic range needs for their work.

Figure 1. Predicted precision and linear range of the Poisson distribution.

Each graph is generated mathematically based on the Poisson distribution for dPCR reactions with 4 different numbers of partitions (n). The majority of dPCR instruments offer reactions where n > 10 000. (A) The relative uncertainty (based on modeling the 95% upper confidence limit of λ) is highest at the extremes of the range (very few positive partitions; k/n  0.95). As the number of partitions in a reaction increases, so does the precision at a given proportion of positive partitions. Once n > 10 000 the relative uncertainty is <5% for the majority of the range. Horizontal dotted lines correspond to a relative uncertainty of 5% and 10%. (B) The dynamic range is proportional to the number of partitions in a reaction, note the dynamic range is greater than the number of partitions. The horizontal dotted lines correspond to the copies per reaction for each given n where the relative uncertainty of λ is less than 50%.

Clin Chem, Volume 66, Issue 8, August 2020, Pages 1012–1029.
Unless provided in the caption above, the following copyright applies to the content of this figure: © American Association for Clinical Chemistry 2020.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Looking to reap the benefits of more microchambers per reaction?

Read our tech note about increasing the number of microreactions per sample by leveraging digital pooling on the QuantStudio Absolute Q dPCR System.

Why does volume matter?

The requirement for dPCR instruments to provide equal volume in each microreaction is critical for ensuring the accuracy and precision of dPCR quantification (The dMIQE Group, 2020). Maintaining equal volume in microreactions is essential for several reasons:

  1. Poisson statistical model: The quantification of target molecules in dPCR relies on the Poisson statistical model. For this model to hold true, each reaction must have an equal chance of containing a single target molecule or being empty. Deviations in reaction volumes could introduce biases in the distribution of target molecules, leading to inaccurate quantification results.
  2. Overall precision: Equal reaction volumes contribute to the precision of dPCR quantification. The Poisson statistical model assumes that the variance in the number of target molecules per microreaction is equal to the mean. Deviations in volumes across all microreactions could lead to increased variance and decreased precision in target quantification.
  3. Consistency in assay performance: When using dPCR for clinical research purposes, consistent assay performance is crucial. Equal volume in each reaction contributes to the reproducibility and robustness of the assay, enhancing the reliability of results over time and between different instruments.

An optimized dPCR assay is a happy dPCR assay

Clear discrimination between positive and negative reactions

The requirement for dPCR instruments to provide clear discrimination between positive and negative microreactions is critical for accurate target quantification and reliable data interpretation (The dMIQE Group, 2020). Clear discrimination between positive and negative microreactions is essential for several reasons:

  1. Accurate target detection: The primary purpose of dPCR is to accurately detect and quantify the presence of target molecules in a sample. Clear discrimination between positive (containing the target) and negative (not containing the target) microreactions is crucial to ensure that the correct number of target molecules is identified and measured.
  2. Avoiding false positives and negatives: Clear discrimination helps to minimize the risk of false positives (erroneously identifying a microreaction as positive) and false negatives (failing to detect a positive microreaction). False results can lead to inaccurate conclusions and misinterpretation of experimental data.
  3. Reducing the amount of “rain” in scatter plots: Microchambers with intermediate levels of fluorescence result in what is considered “rain”. dPCR instruments should have reliable and adjustable thresholding capabilities to accurately define the limit between positive and negative microchambers to account for possible sub-optimal amplification efficiencies.
  4. Improving translational research applications: In clinical research and translational research settings, accurate target detection is crucial for identifying disease-related markers and monitoring with quick turnaround times. Clear discrimination enables the sensitive and specific detection of target molecules, enhancing the translational research capabilities of dPCR.

Our QuantStudio Absolute Q dPCR System meets the requirements as set by the dMIQE guidelines and can help improve your dPCR workflow and analysis.


The dMIQE Group. (2020). The digital MIQE guidelines update: Minimum information for publication of quantitative digital PCR experiments for 2020. Clinical Chemistry, 66(8), 1012-1029.

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