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Figure 2 | BMC Bioinformatics

Figure 2

From: FCI: an R-based algorithm for evaluating uncertainty of absolute real-time PCR quantification

Figure 2

Panel A – D show the FCI output for example A-D, respectively. The FCI output provides the following information: Anova table, summarizes the results of the analysis of variance for the linear regression model underlying the standard curve; Regression Coefficient estimates tables, reports estimates of the standard curve parameters (Intercept and Slope) together with their 95% confidence interval; y0, ct mean of the unknown sample; X0, Unknown concentration estimate in common logarithmic scale; Conc, Unknown concentration estimate in its original scale as copy number; Confidence.level, the chosen confidence level (1-α) of the Fieller's confidence interval; X.lower and X.upper, Lower and upper limits of the 100(1-α)% Fieller's confidence interval of the unknown concentration in logarithmic scale; Conc.lower and Conc.upper Lower and upper limits of the 100(1-α)% Fieller's confidence interval of the unknown concentration in original scale as copy number.

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