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Digital PCR modeling for maximal sensitivity, dynamic range and measurement precision.

Majumdar N, Wessel T, Marks J - PLoS ONE (2015)

Bottom Line: A mathematical framework is presented elucidating the relationships between precision, dynamic range, number of partitions, interrogated volume, and sensitivity in digital PCR.The simulations provide insight to the practitioner on how to adapt experimental loading concentrations to counteract any one of these conditions.An example experiment demonstrating the capabilities of the framework is presented enabling detection across 3.33 logs of starting copy concentration.

View Article: PubMed Central - PubMed

Affiliation: Thermo Fisher Scientific, South San Francisco, CA, 94080, United States of America.

ABSTRACT
The great promise of digital PCR is the potential for unparalleled precision enabling accurate measurements for genetic quantification. A challenge associated with digital PCR experiments, when testing unknown samples, is to perform experiments at dilutions allowing the detection of one or more targets of interest at a desired level of precision. While theory states that optimal precision (Po) is achieved by targeting ~1.59 mean copies per partition (λ), and that dynamic range (R) includes the space spanning one positive (λL) to one negative (λU) result from the total number of partitions (n), these results are tempered for the practitioner seeking to construct digital PCR experiments in the laboratory. A mathematical framework is presented elucidating the relationships between precision, dynamic range, number of partitions, interrogated volume, and sensitivity in digital PCR. The impact that false reaction calls and volumetric variation have on sensitivity and precision is next considered. The resultant effects on sensitivity and precision are established via Monte Carlo simulations reflecting the real-world likelihood of encountering such scenarios in the laboratory. The simulations provide insight to the practitioner on how to adapt experimental loading concentrations to counteract any one of these conditions. The framework is augmented with a method of extending the dynamic range of digital PCR, with and without increasing n, via the use of dilutions. An example experiment demonstrating the capabilities of the framework is presented enabling detection across 3.33 logs of starting copy concentration.

No MeSH data available.


Related in: MedlinePlus

Lower Limit of Detection dependence on False Positive Rate.Lower limit of detection is influenced most heavily by false positives. This dependence is due to limited effect false negatives have in counter balancing false positives due to the limited number of positives at lower concentrations. The plot demonstrates the effects of false reaction call rates on the lower limit of detection for 20,000 partitions at 20% precision. Using a baseline of 0% false positives and 0% false negatives, the lower limit of detection is measured at 0.006 copies/partition. Raising the false positive rate to 1% results in the raising of the lower limit of detection to 0.065 copies/partition, over an order of magnitude elevation.
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pone.0118833.g007: Lower Limit of Detection dependence on False Positive Rate.Lower limit of detection is influenced most heavily by false positives. This dependence is due to limited effect false negatives have in counter balancing false positives due to the limited number of positives at lower concentrations. The plot demonstrates the effects of false reaction call rates on the lower limit of detection for 20,000 partitions at 20% precision. Using a baseline of 0% false positives and 0% false negatives, the lower limit of detection is measured at 0.006 copies/partition. Raising the false positive rate to 1% results in the raising of the lower limit of detection to 0.065 copies/partition, over an order of magnitude elevation.

Mentions: Fig. 7 demonstrates that as the false positive rate increases, the sensitivity at the lowest limit of detection also increases. For tracking the lowest limit of detection, the concentration of interest is where most wells are negative. On average for 20K wells, X% false positives would mean X*20,000/100 positives. A 1% false positive rate results in about 200 false positives. False negatives could be expected to counterbalance the effect of the false positives. However, these false negatives rates are not high enough to really impact the few positives that go toward determining the lowest limit of detection.


Digital PCR modeling for maximal sensitivity, dynamic range and measurement precision.

Majumdar N, Wessel T, Marks J - PLoS ONE (2015)

Lower Limit of Detection dependence on False Positive Rate.Lower limit of detection is influenced most heavily by false positives. This dependence is due to limited effect false negatives have in counter balancing false positives due to the limited number of positives at lower concentrations. The plot demonstrates the effects of false reaction call rates on the lower limit of detection for 20,000 partitions at 20% precision. Using a baseline of 0% false positives and 0% false negatives, the lower limit of detection is measured at 0.006 copies/partition. Raising the false positive rate to 1% results in the raising of the lower limit of detection to 0.065 copies/partition, over an order of magnitude elevation.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4373789&req=5

pone.0118833.g007: Lower Limit of Detection dependence on False Positive Rate.Lower limit of detection is influenced most heavily by false positives. This dependence is due to limited effect false negatives have in counter balancing false positives due to the limited number of positives at lower concentrations. The plot demonstrates the effects of false reaction call rates on the lower limit of detection for 20,000 partitions at 20% precision. Using a baseline of 0% false positives and 0% false negatives, the lower limit of detection is measured at 0.006 copies/partition. Raising the false positive rate to 1% results in the raising of the lower limit of detection to 0.065 copies/partition, over an order of magnitude elevation.
Mentions: Fig. 7 demonstrates that as the false positive rate increases, the sensitivity at the lowest limit of detection also increases. For tracking the lowest limit of detection, the concentration of interest is where most wells are negative. On average for 20K wells, X% false positives would mean X*20,000/100 positives. A 1% false positive rate results in about 200 false positives. False negatives could be expected to counterbalance the effect of the false positives. However, these false negatives rates are not high enough to really impact the few positives that go toward determining the lowest limit of detection.

Bottom Line: A mathematical framework is presented elucidating the relationships between precision, dynamic range, number of partitions, interrogated volume, and sensitivity in digital PCR.The simulations provide insight to the practitioner on how to adapt experimental loading concentrations to counteract any one of these conditions.An example experiment demonstrating the capabilities of the framework is presented enabling detection across 3.33 logs of starting copy concentration.

View Article: PubMed Central - PubMed

Affiliation: Thermo Fisher Scientific, South San Francisco, CA, 94080, United States of America.

ABSTRACT
The great promise of digital PCR is the potential for unparalleled precision enabling accurate measurements for genetic quantification. A challenge associated with digital PCR experiments, when testing unknown samples, is to perform experiments at dilutions allowing the detection of one or more targets of interest at a desired level of precision. While theory states that optimal precision (Po) is achieved by targeting ~1.59 mean copies per partition (λ), and that dynamic range (R) includes the space spanning one positive (λL) to one negative (λU) result from the total number of partitions (n), these results are tempered for the practitioner seeking to construct digital PCR experiments in the laboratory. A mathematical framework is presented elucidating the relationships between precision, dynamic range, number of partitions, interrogated volume, and sensitivity in digital PCR. The impact that false reaction calls and volumetric variation have on sensitivity and precision is next considered. The resultant effects on sensitivity and precision are established via Monte Carlo simulations reflecting the real-world likelihood of encountering such scenarios in the laboratory. The simulations provide insight to the practitioner on how to adapt experimental loading concentrations to counteract any one of these conditions. The framework is augmented with a method of extending the dynamic range of digital PCR, with and without increasing n, via the use of dilutions. An example experiment demonstrating the capabilities of the framework is presented enabling detection across 3.33 logs of starting copy concentration.

No MeSH data available.


Related in: MedlinePlus