ArticleBiomolecular detection and quantification2016
Methods for comparing multiple digital PCR experiments.
Article in Biomolecular detection and quantification, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Developing centrifugal force real-time digital PCR for detecting extremely low DNA concentration.Scientific reports · 2024Article
- Quantification of DNA through the NanoDrop Spectrophotometer: Methodological Validation Using Standard Reference Material and Sprague Dawley Rat and Human DNA.International journal of analytical chemistry · 2020Article
- Accurate design of translational output by a neural network model of ribosome distribution.Nature structural & molecular biology · 2018Article
- Robust Multiplexed Clustering and Denoising of Digital PCR Assays by Data Gridding.Analytical chemistry · 2017Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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