Inference for Quantitation Parameters in Polymerase Chain Reactions via Branching Processes With Random Effects

Abstract

The quantitative polymerase chain reaction (qPCR) is a widely used tool for gene quantitation and has been applied extensively in several scientific areas. The current methods used for analyzing qPCR data fail to account for multiple sources of variability present in the PCR dynamics, leading to biased estimates and incorrect inference. In this article, we introduce a branching process model with random effects to account for within-reaction and between-reaction variability in PCR experiments. We describe, in terms of the observed fluorescence data, new statistical methodology for gene quantitation. Using simulations, PCR experiments, and asymptotic theory we demonstrate the improvements achieved by our methodology compared to existing methods. This article has supplemental materials online.