Métodos estatísticos na análise de dados de PCR em tempo real

Abstract

The qPCR is a widely technique used in the experiments related to gene expression quantification. However, in several cases, statistical formalism like significance tests, p-value and confidence intervals are not used in the data analysis from experiments which involve the qPCR technique. Thus, lack of statistical formalism leads to invalid inferences. The purpose of this work is to present and to compare different methods to analyse gene expression data obtained by using the qPCR. Data from an experiment with coffe plants under water-deficit conditions were used to evaluate the methods. The methods which were evaluate in this study were: efficiency calibrated model, comparative C q method, analysis of variance considering fixed and mixed models. The ratios, which represent the relative expressions, with the respective confidence intervals were obtained and used to compare different methods. The analyses had been done using the softwares R and SAS. The results indicated that the ratio values obtained by the comparative Cq method, efficiency calibrated model, fixed and mixed models are close. However, the best results were obtained using the mixed models, because the width of de confidence intervals were the smallest. Thus, the precision of the estimated effects was better than the estimates obtained by the other methods.