The Reliability and Validity of Real-time PCR Data in Biomedical Sciences

Abstract or Introduction

Despite a fairly broad implementation and application of real-time PCR, there still exists a vacuum in determining the correct procedures for the examination of quantitative real-time PCR; more explicitly, there is a need to determine appropriate procedures to attain the right kind of statistical treatment. In today’s various methods of data analysis, the key statistical inferences are not as exclusive as required like confidence interval. This paper presents and tends to relate four statistical models and approaches on the basis of standard curve method and methods for data analysis.
The first approach developed a multiple regression analysis model for the determination of ∆∆Ct directly from the approximation of interface of gene and treatment paraphernalia. The second approach used the analysis of covariance i.e. ANCOVA model where the derivation of ∆∆Ct could be made through the sequential evaluation and analysis of effects of concurrent variables. The remainder of the models chiefly involves the calculation of ∆Ct subsequently connected through the non-parametric comparable Wilcoxon test and a two group T-test. Moreover, a data quality control model was established, which was then applied through the SAS programs determined for all of the aforementioned approaches; analyzed data output was also presented for a sample set.
The SAS programs were used to put forward practical statistical solutions for real-time PCR data while the programs were also utilized to analyze a sample dataset. After a comprehensive analysis conducted through the approaches and models mentioned above, similar results were obtained.