Reverse transcription (RT), followed by quantitative polymerase chain reaction (qPCR) has been extensively used to study the transcriptional activity of candidate genes and in the validation of microarray experiments. In this paper we present a general linear mixed model methodology for the analysis of qRT-PCR data and use simulations to compare it to the classical data analysis approaches. Similarly to other methods, our model yielded unbiased estimates of the fold change, but it provided a correct type I error rate and coverage of the confidence intervals. Contrarily, other methods either overstated the significance (under null hypothesis) or provided less power for the detection of significant comparisons (under alternative hypothesis).
Proceedings of the World Congress on Genetics Applied to Livestock Production, Volume , , 23.04, 2006
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