Related Experiment Video
Updated: Jan 31, 2026

Digital PCR-based Competitive Index for High-throughput Analysis of Fitness in Salmonella
Published on: May 13, 2019
Statistical Analysis of Nonuniform Volume Distributions for Droplet-Based Digital PCR Assays
Gloria S Yen1, Bryant S Fujimoto1, Thomas Schneider1
1Department of Chemistry , University of Washington , Seattle , Washington 98195-1700 , United States.
Abstract:
We present a method to determine the concentration of nucleic acids in a sample by partitioning it into droplets with a nonuniform volume distribution. This digital PCR method requires no special equipment for partitioning, unlike other methods that require nearly identical volumes. Droplets are generated by vortexing a sample in an immiscible oil to create an emulsion. PCR is performed, and droplets in the emulsion are imaged. Droplets with one or more copies of a nucleic acid are identified, and the nucleic acid concentration of the sample is determined. Numerical simulations of droplet distributions were used to estimate measurement error and dynamic range and to examine the effects of the total volume of droplets imaged and the shape of the droplet size distribution on measurement accuracy. The ability of the method to resolve 1.5- and 3-fold differences in concentration was assessed by using simulations of statistical power. The method was validated experimentally; droplet shrinkage and fusion during amplification were also assessed experimentally and showed negligible effects on measured concentration.
Related Concept Videos
Volume of Distribution
Drug Distribution: Volume of Distribution
Statistical Analysis: Overview
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Statistical Analysis System (SAS)
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
Statistical Significance
Noncompartmental Analysis: Statistical Moment Theory

