A powerful and flexible linear mixed model framework for the analysis of relative quantification RT-PCR data

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is currently viewed as the most precise technique to quantify levels of messenger RNA. Relative quantification compares the expression of a target gene under two or more experimental conditions normalized to the measured expression of a control gene. The statistical methods and software currently available for the analysis of relative quantification of RT-PCR data lack the flexibility and statistical properties to produce valid inferences in a wide range of experimental situations. In this paper we present a novel method for the analysis of relative quantification of qRT-PCR data, which consists of the analysis of cycles to threshold values (C(T)) for a target and a control gene using a general linear mixed model methodology. Our method allows testing of a broader class of hypotheses than traditional analyses such as the classical comparative C(T). Moreover, a simulation study using plasmode datasets indicated that the estimated fold-change in pairwise comparisons was the same using either linear mixed models or a comparative C(T) method, but the linear mixed model approach was more powerful. In summary, the method presented in this paper is more accurate, powerful and flexible than the traditional methods for analysis of qRT-PCR data. This new method is especially useful for studies involving multiple experimental factors and complex designs.