Methods for comparing multiple digital PCR experiments

Michał Burdukiewicz; Stefan Rödiger; Piotr Sobczyk; Mario Menschikowski; Peter Schierack; Paweł Mackiewicz

doi:10.1016/j.bdq.2016.06.004

Methods for comparing multiple digital PCR experiments

Biomol Detect Quantif. 2016 Aug 10:9:14-9. doi: 10.1016/j.bdq.2016.06.004. eCollection 2016 Sep.

Authors

Michał Burdukiewicz¹, Stefan Rödiger², Piotr Sobczyk³, Mario Menschikowski⁴, Peter Schierack², Paweł Mackiewicz¹

Affiliations

¹ University of Wroclaw, Faculty of Biotechnology, Department of Genomics, Wroclaw, Poland.
² Institute of Biotechnology, Brandenburg University of Technology Cottbus - Senftenberg, Senftenberg, Germany.
³ Wrocław University of Technology, Institute of Mathematics and Computer Science, Poland.
⁴ Dresden University of Technology, Institute of Clinical Chemistry and Laboratory Medicine, Germany.

Abstract

The estimated mean copy per partition (λ) is the essential information from a digital PCR (dPCR) experiment because λ can be used to calculate the target concentration in a sample. However, little information is available how to statistically compare dPCR runs of multiple runs or reduplicates. The comparison of λ values from several runs is a multiple comparison problem, which can be solved using the binary structure of dPCR data. We propose and evaluate two novel methods based on Generalized Linear Models (GLM) and Multiple Ratio Tests (MRT) for comparison of digital PCR experiments. We enriched our MRT framework with computation of simultaneous confidence intervals suitable for comparing multiple dPCR runs. The evaluation of both statistical methods support that MRT is faster and more robust for dPCR experiments performed in large scale. Our theoretical results were confirmed by the analysis of dPCR measurements of dilution series. Both methods were implemented in the dpcR package (v. 0.2) for the open source R statistical computing environment.

Keywords: Digital PCR; GLM; Generalized Linear Models; Multiple comparison; dPCR.