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

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 C_t (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 C_t model have been statistically tested and explained. Several methods were tested to compare control with treatment means of C_t. 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.

KEYWORDS:

• Real-time PCR data
• in silico
• gene expression
• statistical analysis
• SAS procedures

Acknowledgements

The authors would like to specially thank Excellent Center in Animal Science of Ferdowsi University of Mashhad for kindly providing necessary facilities and equipment.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Mohammadreza Nassiri http://orcid.org/0000-0001-7119-8155

Mahdi Elahi Torshizi http://orcid.org/0000-0002-1574-3865

Shahrokh Ghovvati http://orcid.org/0000-0002-2016-2184

Mohammad Doosti http://orcid.org/0000-0002-2728-8265

Additional information

Funding

This work was supported by the Agricultural Faculty of Ferdowsi University of Mashhad, Iran [grant number 368P].