pcrbatch {qpcR}R Documentation

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, and expfit on a dataframe containing many qPCR runs. The input can also be a list obtained from modlist, which simplifies things in many cases. 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 result is automatically copied to the clipboard.

Usage

pcrbatch(x, cols = NULL, model = l4, group = NULL, type = "cpD2",
         opt = FALSE, smooth = c("none", "tukey", "lowess"), norm = FALSE, 
         fact = 1, ave = c("mean", "median"), backsub = NULL, 
         retPar = FALSE, crit, ...) 

Arguments

x a dataframe containing the qPCR raw data from the different runs or a list obtained from modlist.
cols the columns (runs) to be analyzed. If NULL, all runs will be considered.
group a vector containing the grouping for possible replicates.
model the model to be used.
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. Either Tukey´s running median or non-parametric lowess smoothing.
norm logical. Normalization of the raw data within [0, 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".
backsub background subtraction. If NULL, not applied. Otherwise, a numeric sequence such as 1:10. See 'Details'.
retPar logical. Should the parameters from the fit be included in the output?
crit the criterium for model selection. See mselect.
... other parameters to be passed to downstream methods.

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. "group_1" 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. Background subtraction is done by averaging the backsub cycles of the run and subtracting this from all data points. In case of unsuccessful model fitting, the run is skipped and the next run is analyzed.

Value

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

Note

When subsequent use of ratiocalc is desired, use pcrbatch on the single run level with group = NULL, otherwise error propagation will fail.

Author(s)

Andrej-Nikolai Spiess

References

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

See Also

The function modlist for creating a list of models.

Examples

## complete dataset
## Not run: 
temp <- pcrbatch(reps)
## End(Not run)

## first 4 runs and return parameters of fit
## do background subtraction using the first 8 cycles
res1 <- pcrbatch(reps, 2:4, retPar = TRUE, backsub = 1:8)

##  first 8 runs, with 4 replicates each, l5 model
res2 <- pcrbatch(reps, 2:9, model = l5, c(1,1,1,1,2,2,2,2))

## using model selection (likelihood ratio) on the first 4 runs, 
## run 1+2 are replicates
res3 <- pcrbatch(reps, 2:5, group = c(1,1,2,3), opt = TRUE, crit = "ratio")

## converting a 'modlist' to 'pcrbatch'
ml <- modlist(reps, 1, 2:5, b3)
res4 <- pcrbatch(ml)      

[Package qpcR version 1.2-4 Index]