User manual

Absolute Quantification and the Statistics of Droplet Digital
™
PCR
36 | Droplet Digital
™
PCR Applications Guide
Errors in ddPCR
Two types of errors are reported by QuantaSoft software: technical errors (Poisson errors)
and total errors.
Technical errors (Poisson errors): a measurement error based on known properties of the
system that can be calculated based on a single well or by pooling all the droplets from
multiple wells. One of the assumptions in this error calculation is that the sample in a
ddPCR well is a subsample from a larger whole. Poisson errors are an excellent estimate
of technical replicate measurement errors. A technical replicate in this context is when
aliquots of the same sample are loaded into multiple ddPCR wells. An interesting and useful
feature of ddPCR is that it is possible to estimate the technical replicate error from a single
concentration measurement.
Total errors: the recommended error to use in most applications in biology. It is the greater
of the technical error and the standard error of the mean. This method is the preferred
one because it prevents underestimation of the error. We can say with certainty that if you
observe a standard error of the mean that is less than the theoretical technical error, you
were just “lucky” and the true error is in reality at least as big as the technical error.
For experiments with replicates, both an empirical error measurement (the total error) and
a theoretical technical replicate error (the Poisson error or technical error) are calculated.
In most cases, it is more appropriate to report the total error measurement. If the wells are
true technical replicates as defined above, the total error and the Poisson error will be nearly
identical for good assays.
Note: Error bars are shown in QuantaSoft software as 95% confidence intervals. The closest
parallel in quantitative PCR (qPCR) is the mean ± 2 times the standard error of the mean.
Some qPCR systems by default show 68% confidence intervals, or the mean ± 1 times the
standard error of the mean.
Note: The theory behind technical errors is explained in Appendix B.