Error Bar Real Time Pcr
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biological replicate - (Dec/20/2011 )Visit this topic http://www.protocol-online.org/biology-forums-2/posts/23815.html in live forum Printer Friendly VersionABI has a step by step guide for qPCR statistics that ultimately gives you the fold http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-8-131 change and the standard deviation (can be found here). My assumption is that the calculated SD only represents the variation in real time technical replicates and not the biological replicates. How would you incorporate the variations in both technical and biological replicates into the final error bars (say SD)? Is the technical replicate SD is reflected in the final result at all? any input is real time pcr highly appreciated. Kaveh -kaveh- From what I have seen, most people just plot technical replicates +/- error bars (95% CIs) and then they indicate that they repeated the experiment 2 or 3 times and had similar results. I think for biological replicates you are correct in assuming that you just ignore the SD for the technical replicates and just calculate the mean and SD for the biological replicates. -doxorubicin- Yes, we take SD of technical replicates as a indication how precise was the experiment, but if we plott biological replicates, we only calculate SD for the biological ones and ignore the technical. -Trof- Visit this topic in BioForum Printer Friendly Version About Terms of Service Privacy Feedback Sponsorship © 1999-2013 Protocol Online, All rights reserved.
Open Access Statistical significance of quantitative PCRYannKarlen1, AlanMcNair1, SébastienPerseguers2, ChristianMazza3 and NicolasMermod1Email authorBMC Bioinformatics20078:131DOI: 10.1186/1471-2105-8-131© Karlen et al; licensee BioMed Central Ltd.2007Received: 22September2006Accepted: 20April2007Published: 20April2007 Abstract Background PCR has the potential to detect and precisely quantify specific DNA sequences, but it is not yet often used as a fully quantitative method. A number of data collection and processing strategies have been described for the implementation of quantitative PCR. However, they can be experimentally cumbersome, their relative performances have not been evaluated systematically, and they often remain poorly validated statistically and/or experimentally. In this study, we evaluated the performance of known methods, and compared them with newly developed data processing strategies in terms of resolution, precision and robustness. Results Our results indicate that simple methods that do not rely on the estimation of the efficiency of the PCR amplification may provide reproducible and sensitive data, but that they do not quantify DNA with precision. Other evaluated methods based on sigmoidal or exponential curve fitting were generally of both poor resolution and precision. A statistical analysis of the parameters that influence efficiency indicated that it depends mostly on the selected amplicon and to a lesser extent on the particular biological sample analyzed. Thus, we devised various strategies based on individual or averaged efficiency values, which were used to assess the regulated expression of several genes in response to a growth factor. Conclusion Overall, qPCR data analysis methods differ significantly in their performance, and this analysis identifies methods that provide DNA quantification estimates of high precision, robustness and reliability. These methods allow reliable estimations of relative expression ratio of two-fold or higher, and our analysis provides an estimation of the number of biological samples that have to be analyzed to achieve a given precision. BackgroundQuantitative PCR is used widely to detect and quantify specific DNA sequences in scientific fields that rang