qpcR (version 1.2-2)

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, 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 data 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.

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 data of class modlist is given, all previous data manipulations (i.e. smoothing or background subtraction) are eliminated as the raw data is taken. Thus, if opt was applied with modlist, this has to be repeated by setting opt = TRUE. 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. Background subtraction is done by averaging the backsub cycles of the run and subtracting this from all data points.

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 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)