An automated approach to classification of duplex assay for digital droplet PCR

In the digital polymerase chain reaction (dPCR) detection process, discriminating positive droplets from negative ones directly affects the final concentration and is one of the most important factors affecting accuracy. Current automated classification methods usually discuss single-channel detections, whereas duplex detection experiments are less discussed. In this paper, we designed a classification method by estimating the upper limit of the negative droplets. The right tail of the negative droplets is approximated using a generalized Pareto distribution. Furthermore, our method takes fluorescence compensation in duplex assays into account. We also demonstrate the method on Bio-Rad's mutant detection dataset. Experimental results show that the method provides similar or better accuracy than other algorithms reported over a wider dynamic range.