The role of partition volume variability, or polydispersity, in digital polymerase chain reaction methods is examined through formal considerations and Monte Carlo simulations. Contrary to intuition, polydispersity causes little precision loss for low average copy number per partition μ and can actually improve precision when μ exceeds ∼4. It does this by negatively biasing the estimates of μ, thus increasing the number of negative (null) partitions N 0 . In keeping with binomial statistics, this increases the relative precision of N 0 and hence of the biased estimate m of μ. Below μ = 1, the precision loss and the bias are both small enough to be negligible for many applications. For higher μ the bias becomes more important than the imprecision, making accuracy dependent on knowledge of the partition volume distribution function. This information can be gained with optical microscopy or through calibration with reference materials.
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Joel Tellinghuisen (2016) studied this question.
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