Methods for comparing multiple digital PCR experiments

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

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.
Author Michał Burdukiewicz (FMIS / DCSDCAM) - [University of Wrocław (UWr)]
Michał Burdukiewicz,,
- Department of CAD/CAM Systems Design and Computer-Aided Medicine
- Uniwersytet Wrocławski
, Stefan Rödiger
Stefan Rödiger,,
-
, Piotr Sobczyk
Piotr Sobczyk,,
-
, Mario Menschikowski
Mario Menschikowski,,
-
, Peter Schierack
Peter Schierack,,
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, Paweł Mackiewicz
Paweł Mackiewicz,,
-
Journal seriesBiomolecular Detection and Quantification, ISSN , e-ISSN 2214-7535
Issue year2016
Vol9
Pages14-19
Publication size in sheets0.5
Keywords in EnglishDigital PCR; GLM; Generalized Linear Models; Multiple comparison; dPCR
ASJC Classification1312 Molecular Biology; 1313 Molecular Medicine; 1303 Biochemistry; 1315 Structural Biology
DOIDOI:10.1016/j.bdq.2016.06.004
URL https://www.ncbi.nlm.nih.gov/pubmed/27551672
Languageen angielski
Score (nominal)5
ScoreMinisterial score = 0.0, 04-09-2019, ArticleFromJournal
Ministerial score (2013-2016) = 5.0, 04-09-2019, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.547
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