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Evaluation of different statistical methods using SAS software: an in silico approach for analysis of real-time PCR data

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  • Mohammadreza Nassiri
  • Mahdi Elahi Torshizi
  • Shahrokh Ghovvati
  • Mohammad Doosti

Abstract

Real-time polymerase chain reaction (PCR) is reliable quantitative technique in gene expression studies. The statistical analysis of real-time PCR data is quite crucial for results analysis and explanation. The statistical procedures of analyzing real-time PCR data try to determine the slope of regression line and calculate the reaction efficiency. Applications of mathematical functions have been used to calculate the target gene relative to the reference gene(s). Moreover, these statistical techniques compare Ct (threshold cycle) numbers between control and treatments group. There are many different procedures in SAS for real-time PCR data evaluation. In this study, the efficiency of calibrated model and delta delta Ct model have been statistically tested and explained. Several methods were tested to compare control with treatment means of Ct. The methods tested included t-test (parametric test), Wilcoxon test (non-parametric test) and multiple regression. Results showed that applied methods led to similar results and no significant difference was observed between results of gene expression measurement by the relative method.

Suggested Citation

  • Mohammadreza Nassiri & Mahdi Elahi Torshizi & Shahrokh Ghovvati & Mohammad Doosti, 2018. "Evaluation of different statistical methods using SAS software: an in silico approach for analysis of real-time PCR data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(2), pages 306-319, January.
    • Handle: RePEc:taf:japsta:v:45:y:2018:i:2:p:306-319
    DOI: 10.1080/02664763.2016.1276890
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