Statistical analysis of real-time PCR data

Background: 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. Results: In the first approach, a multiple regression analysis model was developed to derive Ct from estimation of interaction of gene and treatment effects. In the second approach, an ANCOVA (analysis of covariance) model was proposed, and the Ct can be derived from analysis of effects of variables. The other two models involve calculation Ct followed by a two group t-test and nonparametric analogous Wilcoxon test. SAS programs were developed ...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Joshua S. Yuan, Ann Reed, Feng Chen, C. Neal Stewart Jr.
• Keywords: Analysis, BMCBI 2006, Real-time Pcr, Real-time Pcr Data