02587nas a2200277 4500008003900000245007200039210006900111260001300180300001200193490000700205520165700212653002201869653002201891653002001913653003201933653001401965653003101979653000902010653001802019653001802037653002202055100002302077700002002100700001902120856017002139 2010 d00aStatistical aspects of quantitative real-time PCR experiment design0 aStatistical aspects of quantitative realtime PCR experiment desi bElsevier a231-2360 v503 aExperiments using quantitative real-time PCR to test hypotheses are limited by technical and biological variability; we seek to minimise sources of confounding variability through optimum use of biological and technical replicates. The quality of an experiment design is commonly assessed by calculating its prospective power. Such calculations rely on knowledge of the expected variances of the measurements of each group of samples and the magnitude of the treatment effect; the estimation of which is often uninformed and unreliable. Here we introduce a method that exploits a small pilot study to estimate the biological and technical variances in order to improve the design of a subsequent large experiment. We measure the variance contributions at several ‘levels’ of the experiment design and provide a means of using this information to predict both the total variance and the prospective power of the assay. A validation of the method is provided through a variance analysis of representative genes in several bovine tissue-types. We also discuss the effect of normalisation to a reference gene in terms of the measured variance components of the gene of interest. Finally, we describe a software implementation of these methods, powerNest, that gives the user the opportunity to input data from a pilot study and interactively modify the design of the assay. The software automatically calculates expected variances, statistical power, and optimal design of the larger experiment. powerNest enables the researcher to minimise the total confounding variance and maximise prospective power for a specified maximum cost for the large study.10aError propagation10aExperiment design10aGene expression10aNested analysis of variance10apowerNest10aProspective power analysis10aqPCR10aReal-time PCR10aSampling plan10aStatistical power1 aKitchen, Robert, R1 aKubista, Mikael1 aTichopad, Ales uhttp://www.sciencedirect.com/science?_ob=GatewayURL&_method=citationSearch&_uoikey=B6WN5-4Y88DBN-1&_origin=SDEMFRHTML&_version=1&md5=7bb0b5b797d6e1f7c5c2df478fc88e5a