Statistical analysis of real-time PCR data

TLDR

Real‑time PCR is widely used, yet current data processing lacks explicit statistical treatment, especially regarding confidence intervals and significance. The study presents and compares four statistical approaches/models for real‑time PCR data analysis. The authors developed four models—a multiple‑regression model, an ANCOVA model, a DeltaCt‑t‑test model, and a DeltaCt‑Wilcoxon model—implemented them in SAS, and added a data‑quality control module. The models produced comparable results, and the proposed procedures provide reliable statistical elements for estimating relative gene expression.

Abstract

Even though real-time PCR has been broadly applied in biomedical sciences, data processing procedures for the analysis of quantitative real-time PCR are still lacking; specifically in the realm of appropriate statistical treatment. Confidence interval and statistical significance considerations are not explicit in many of the current data analysis approaches. Based on the standard curve method and other useful data analysis methods, we present and compare four statistical approaches and models for the analysis of real-time PCR data.In the first approach, a multiple regression analysis model was developed to derive DeltaDeltaCt from estimation of interaction of gene and treatment effects. In the second approach, an ANCOVA (analysis of covariance) model was proposed, and the DeltaDeltaCt can be derived from analysis of effects of variables. The other two models involve calculation DeltaCt followed by a two group t-test and non-parametric analogous Wilcoxon test. SAS programs were developed for all four models and data output for analysis of a sample set are presented. In addition, a data quality control model was developed and implemented using SAS.Practical statistical solutions with SAS programs were developed for real-time PCR data and a sample dataset was analyzed with the SAS programs. The analysis using the various models and programs yielded similar results. Data quality control and analysis procedures presented here provide statistical elements for the estimation of the relative expression of genes using real-time PCR.