Statistical models for the analysis and design of digital polymerase chain (dPCR) experiments

Analytical Chemistry
By:  and 

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Abstract

Statistical methods for the analysis and design of experiments using digital PCR (dPCR) have received only limited attention and have been misused in many instances. To address this issue and to provide a more general approach to the analysis of dPCR data, we describe a class of statistical models for the analysis and design of experiments that require quantification of nucleic acids. These models are mathematically equivalent to generalized linear models of binomial responses that include a complementary, log–log link function and an offset that is dependent on the dPCR partition volume. These models are both versatile and easy to fit using conventional statistical software. Covariates can be used to specify different sources of variation in nucleic acid concentration, and a model’s parameters can be used to quantify the effects of these covariates. For purposes of illustration, we analyzed dPCR data from different types of experiments, including serial dilution, evaluation of copy number variation, and quantification of gene expression. We also showed how these models can be used to help design dPCR experiments, as in selection of sample sizes needed to achieve desired levels of precision in estimates of nucleic acid concentration or to detect differences in concentration among treatments with prescribed levels of statistical power.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Statistical models for the analysis and design of digital polymerase chain (dPCR) experiments
Series title Analytical Chemistry
DOI 10.1021/acs.analchem.5b02429
Volume 87
Issue 21
Year Published 2015
Language English
Publisher American Chemical Society
Publisher location Washington, DC
Contributing office(s) Wetland and Aquatic Research Center
Description 8 p.
First page 10886
Last page 10893
Online Only (Y/N) N
Additional Online Files (Y/N) N