Real-time quantitative polymerase chain reaction (qPCR) is widely used in biomedical sciences quantifying its results through the relative expression (RE) of a target gene versus a reference one. Obtaining significance levels for RE assuming an underlying probability distribution of the data may be difficult to assess. We have developed the web-based application BootstRatio, which tackles the statistical significance of the RE and the probability that RE>1 through resampling methods without any assumption on the underlying probability distribution for the data analyzed. BootstRatio perform these statistical analyses of gene expression ratios in two settings: (1) when data have been already normalized against a control sample and (2) when the data control samples are provided. Since the estimation of the probability that RE>1 is an important feature for this type of analysis, as it is used to assign statistical significance and it can be also computed under the Bayesian framework, a simulation study has been carried out comparing the performance of BootstRatio versus a Bayesian approach in the estimation of that probability. In addition, two analyses, one for each setting, carried out with data from real experiments are presented showing the performance of BootstRatio. Our simulation study suggests that Bootstratio approach performs better than the Bayesian one excepting in certain situations of very small sample size (N≤12). The web application BootstRatio is accessible through http://regstattools.net/br and developed for the purpose of these intensive computation statistical analyses.
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