Comput. Biol. Med. - BootstRatio: A web-based statistical analysis of fold-change in qPCR and RT-qPCR data using resampling methods.

Tópicos

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Resumo

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.

Resumo Limpo

realtim quantit polymeras chain reaction qpcr wide use biomed scienc quantifi result relat express re target gene versus refer one obtain signific level re assum under probabl distribut data may difficult assess develop webbas applic bootstratio tackl statist signific re probabl re resampl method without assumpt under probabl distribut data analyz bootstratio perform statist analys gene express ratio two set data alreadi normal control sampl data control sampl provid sinc estim probabl re import featur type analysi use assign statist signific can also comput bayesian framework simul studi carri compar perform bootstratio versus bayesian approach estim probabl addit two analys one set carri data real experi present show perform bootstratio simul studi suggest bootstratio approach perform better bayesian one except certain situat small sampl size n web applic bootstratio access httpregstattoolsnetbr develop purpos intens comput statist analys

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