qpcR (version 1.2-0)

curvemean: Building a model which averages a batch of qPCR curves

Description

Starting with a batch of qPCR curves from class modlist, the function builds a sigmoidal model which averages the cycle numbers of the curves at every y-value (raw fluorescence). Thus, in contrast to existing qPCR averaging methods, not only the efficiencies or threshold cycles (as can be obtained from multiple reference genes) are averaged, but the complete structure of the batch at every fluorescence value. Beware: This is curve averaging, NOT model averaging!

Usage

curvemean(ml, mean = c("amean", "gmean", "hmean"), 
          model = l4, which = 1, plot = TRUE)

Arguments

ml

a qPCR batch object of class 'modlist'.

mean

the averaging function, see 'Details'. Default is the arithmetic mean.

model

the sigmoidal model to be used for the averaged curve.

which

the position of the curve in the list which is used for defining the y-values. Defaults to the first curve.

plot

should results be plotted?

Value

• A model of class 'pcrfit' from the averaged curve. The points from the averaged curve (mean.x) and the standard deviation (sd.x) are added as items.

Details

Starting from the raw fluorescence values of a defined curve in the list, the cycle number of all other curves at this value are calculated and averaged. After that, a new sigmoidal model is fit to the obtained data. Note: often works better (more data points can be averaged) if norm = TRUE when buidling the model list with modlist. Three different averaging functions can be used: Arithmetic mean: $$\bar x = \frac{\sum_{i=1}^nx_i}{n}$$ Geometric mean: $$\bar x = \left(\prod_{i=1}^nx_i \right)^\frac{1}{n}$$ Harmonic mean: $$\bar x = \frac{n}{\sum_{i=1}^n\frac{1}{x_i}}$$

References

Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Vandesompele et al., Genome Biology, 2002, 3: research0034.1-0034.11.

Examples

## create arithmetic mean curve of four
## serial dilutions
ml <- modlist(reps, fluo = c(2, 6, 10, 14))
curvemean(ml)

## effect of normalizing data to [0, 1]:
## more averaged datapoints
ml <- modlist(reps, fluo = c(2, 6, 10, 14), norm = TRUE)
curvemean(ml)

## difference with geometric mean
curvemean(ml, mean = "gmean")