02352nas a2200193 4500008003900000245008300039210006900122260001300191300001300204490000700217520155300224100001901777700001601796700002001812700002301832700002001855700002301875856026001898 2010 d00aQuality control for quantitative PCR based on amplification compatibility test0 aQuality control for quantitative PCR based on amplification comp bElsevier a308-312 0 v503 aQuantitative qPCR is a routinely used method for the accurate quantification of nucleic acids. Yet it may generate erroneous results if the amplification process is obscured by inhibition or generation of aberrant side-products such as primer dimers. Several methods have been established to control for pre-processing performance that rely on the introduction of a co-amplified reference sequence, however there is currently no method to allow for reliable control of the amplification process without directly modifying the sample mix. Herein we present a statistical approach based on multivariate analysis of the amplification response data generated in real-time. The amplification trajectory in its most resolved and dynamic phase is fitted with a suitable model. Two parameters of this model, related to amplification efficiency, are then used for calculation of the Z-score statistics. Each studied sample is compared to a predefined reference set of reactions, typically calibration reactions. A probabilistic decision for each individual Z-score is then used to identify the majority of inhibited reactions in our experiments. We compare this approach to univariate methods using only the sample specific amplification efficiency as reporter of the compatibility. We demonstrate improved identification performance using the multivariate approach compared to the univariate approach. Finally we stress that the performance of the amplification compatibility test as a quality control procedure depends on the quality of the reference set.1 aTichopad, Ales1 aBar, Tzachi1 aPecen, Ladislav1 aKitchen, Robert, R1 aKubista, Mikael1 aPfaffl, Michael, W uhttp://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WN5-4Y88DBN-3&_user=10&_coverDate=04%2F30%2F2010&_alid=1247745718&_rdoc=1&_fmt=high&_orig=search&_cdi=6953&_sort=r&_docanchor=&view=c&_ct=2&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md502587nas 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