ddpcRquant: threshold determination for single channel droplet digital PCR experiments
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Digital PCR is rapidly gaining interest in the field of molecular biology for absolute quantification of nucleic acids. However, the first generation of platforms still needs careful validation and requires a specific methodology for data analysis to distinguish negative from positive signals by defining a threshold value. The currently described methods to assess droplet digital PCR (ddPCR) are based on an underlying assumption that the fluorescent signal of droplets is normally distributed. We show that this normality assumption does not likely hold true for most ddPCR runs, resulting in an erroneous threshold. We suggest a methodology that does not make any assumptions about the distribution of the fluorescence readouts. A threshold is estimated by modelling the extreme values in the negative droplet population using extreme value theory. Furthermore, the method takes shifts in baseline fluorescence between samples into account. An R implementation of our method is available, allowing automated threshold determination for absolute ddPCR quantification using a single fluorescent reporter.
KeywordsDroplet digital PCR Data analysis R software Rain Automation Extreme value distribution Threshold determination
Comma separated value
Droplet digital polymerase chain reaction
Digital polymerase chain reaction
HyperText Markup Language
Negative template control
- QQ plot
The authors would like to acknowledge the support of following research grants: Amfar (Group funding, grant 108314-51-RGRL), HIVERA/SBO IWT (Group funding, grant 130442), FWO (Linos Vandekerckhove, grant 1.8.020.09.N.00), IWT (Pawel Bonzckowski, grant 111286; Eva Malatinkova, grant 111393), BOF (Maja Kiselinova, grant 01N02712), King Baudouin Foundation (Group funding, grant 2010-R20640-003), unrestricted grant of Bristol-Myers Squibb Belgium (Group funding), Multidisciplinary Research Partnership Bioinformatics: From Nucleotides to Networks Project (01MR0310W) of Ghent University (Group funding), and IAP research network P7/06 of the Belgian Government (Belgian Science Policy; Group funding).
Conflict of interest
The authors declare to have no competing interests.
Performed the ddPCR runs: MK, EM, KV. Analyzed the ddPCR data: WT, PB, WDS. Developed the statistical methodology: MV, JDN, OT, WT, WDS. Wrote the R code: WT, MV. Wrote the paper: WT, WDS, MV, JDN. Critically read, reviewed, and approved the final version of the manuscript: all authors.
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