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Analytical and Bioanalytical Chemistry

, Volume 407, Issue 19, pp 5827–5834 | Cite as

ddpcRquant: threshold determination for single channel droplet digital PCR experiments

  • Wim Trypsteen
  • Matthijs Vynck
  • Jan De Neve
  • Pawel Bonczkowski
  • Maja Kiselinova
  • Eva Malatinkova
  • Karen Vervisch
  • Olivier Thas
  • Linos VandekerckhoveEmail author
  • Ward De Spiegelaere
Research Paper

Abstract

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.

Keywords

Droplet digital PCR Data analysis R software Rain Automation Extreme value distribution Threshold determination 

Abbreviations

csv

Comma separated value

ddPCR

Droplet digital polymerase chain reaction

dPCR

Digital polymerase chain reaction

HTML

HyperText Markup Language

NTC

Negative template control

QQ plot

Quantile–quantile plot

Notes

Acknowledgments

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.

Authors’ contributions

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.

Supplementary material

216_2015_8773_MOESM1_ESM.pdf (131 kb)
ESM 1 (PDF 131 kb)
216_2015_8773_MOESM2_ESM.pdf (4 mb)
ESM 2 (PDF 4108 kb)
216_2015_8773_MOESM3_ESM.pdf (313 kb)
ESM 3 (PDF 313 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Wim Trypsteen
    • 1
  • Matthijs Vynck
    • 2
  • Jan De Neve
    • 2
  • Pawel Bonczkowski
    • 1
  • Maja Kiselinova
    • 1
  • Eva Malatinkova
    • 1
  • Karen Vervisch
    • 1
  • Olivier Thas
    • 2
    • 3
  • Linos Vandekerckhove
    • 1
    Email author
  • Ward De Spiegelaere
    • 1
  1. 1.Department of Internal Medicine, HIV Translational Research UnitGhent University and University HospitalGhentBelgium
  2. 2.Department of Mathematical Modelling, Statistics and Bio-informaticsGhent UniversityGhentBelgium
  3. 3.National Institute for Applied Statistics Research Australia (NIASRA), School of Mathematics and Applied StatisticsUniversity of WollongongWollongongAustralia

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