qpcR (version 1.3-7.1)

pcropt1: Combinatorial elimination of plateau and ground phase cycles

Description

The estimation of PCR efficiency and calculation of initial fluorescence $F_0$ is analyzed by refitting the (optimized) model on subsets of the data, thereby using all possible combinations of datapoints. The estimated parameters are then collated in a dataframe. This is intended to be the prerequisite for finding the optimal datapoints that minimize the fit or exhibit the best correlation to a calibration curve. This approch is an extension to the method described in Rutledge et al. (2004). The result of any collected parameter can then be displayed by a rank-colored bubbleplot. See 'Examples'.

Usage

pcropt1(object, fact = 3, opt = FALSE, plot = TRUE, bubble = NULL, ...)

Arguments

object

an object of class 'pcrfit'.

fact

numeric. The multiplier for the scan border. See 'Details'.

opt

logical. If true, model selection is applied for each combination of cycles. Beware: Slow!!

plot

logical. If TRUE, the iterative plotting is displayed, which makes the method a bit slower.

bubble

either NULL for no bubble plot or any parameter (given as a character vector) in the result matrix to be displayed as a bubble plot. See 'Examples'.

...

other parameters to be passed on to efficiency, mselect or qpcR:::bubbleplot.

Value

• A matrix with the selected border values, goodness-of-fit measures as obtained from pcrGOF and efficiency and $F_0$ values from efficiency.

Details

It has been shown by Rutledge (2004) that the estimation of PCR efficiency gives more realistic values when the number of plateau cycles are decreased. This paradigm is the basis for this function, but we also consider the cycles in the ground phase and all combinations between ground/plateau cycles. All datapoints between the lower border cpD1 - fact * (cpD1 - cpD2) and upper border cpD1 + fact * (cpD1 - cpD2) are cycled through.

References

Sigmoidal curve fitting redefines quantitative real-time PCR with the prospective of developing automated high-throughput applications. Rutledge RG. Nucleic Acids Research (2004), 32: e178.

Examples

## optimize fit and display
## bubbleplot of R-square
m1 <- pcrfit(reps, 1, 2, l4)
res1 <- pcropt1(m1, plot = FALSE, bubble = "Rsq")