qpcR (version 1.0-6)

pcrbatch: Batch calculation of qPCR efficiency and several other important qPCR parameters with different methods

Description

This function batch calculates the results obtained from efficiency, sliwin, expfit and expfit2 on a dataframe containing many qPCR runs. The output is a dataframe with the estimated parameters and model description. Very easy to use on datasheets containing many qPCR runs, i.e. as can be imported from Excel. The data is automatically copied to the clipboard.

Usage

pcrbatch(x, cols = 2:ncol(x), group = NULL, model = l4(), type = "cpD2", 
opt = TRUE, smooth = "tukey", norm = FALSE, fact = 1, ave = "mean", plot = FALSE)

Arguments

x

a dataframe containing the qPCR raw data from the different runs.

cols

the columns (runs) to be analyzed. Defaults to every run in the dataframe.

group

a vector containing the grouping for possible replicates.

model

the model to be used for the analysis, with the usual 'drc' package nomenclature.

type

the point on the amplification curve from which the efficiency is estimated. See efficiency.

opt

logical. Should model optimization take place? If TRUE, model selection is applied.

smooth

the smoothing algorithm for the data. Defaults to Tukey�s running median. Non-parametric lowess smoothing with "lowess" is another option.

norm

logical. Normalization of the raw data such that the highest value (plateau phase) is 1. See references.

fact

a constant multiplication factor for the raw qPCR data.

ave

averaging method for replicates. Defaults to "mean", another option is "median".

plot

logical. If TRUE, a graphical analysis of the qPCR data is displayed, otherwise (default) a simple console output is displayed.

Value

• A dataframe with the results in columns containing the calculated values with descriptions and the method used as the name prefix.

encoding

latin1

Details

The qPCR raw data should be arranged with the cycle numbers in the first column with the name "Cycles". All subsequent columns must be plain raw data with sensible column descriptions. If replicates are defined, the output will contain a numbering of groups (i.e. "group1" for the first replicate group). The model selection process is optional, but we advocate using this for obtaining better parameter estimates. Normalization has been described to improve certain qPCR analyses, but this has still to be independently evaluated.

References

A standard curve based method for relative real time PCR data processing. Larionov et al., BMC Bioinformatics, 6: 62.

Examples

## complete dataset
temp <- pcrbatch(reps)

## first 4 runs
temp <- pcrbatch(reps, 2:4)

##  first 8 runs, with 4 replicates each
temp <- pcrbatch(reps, 2:9, c(1,1,1,1,2,2,2,2))

## using model selection on the first 4 runs, run 1+2 are replicates
temp <- pcrbatch(reps, 2:5, c(1,1,2,3), opt = TRUE)