RDML (version 0.4-2)

RDML: qPCR data import function

Description

Imports qPCR data from RDML v1.1 format files (Lefever et al. 2009) and transforms it to the appropriate format of the qpcR (Ritz et al. 2008, Spiess et al. 2008) and chiPCR packages. RDML is the recommended file format element in the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines (Bustin et al. 2009). Data can be imported to the one table format (flat) or list of tables spited by PCR targets and samples types (see 'Details'). Tables consist of 'Cycles' in the first column and fluorescence data of the samples in the remaining columns for qPCR data. For melting data tables consist of 'Temperature' in the first column and fluorescence data of the samples in the remaining columns. Names of the samples can be formed by patterns (see 'Details').

Usage

RDML(rdmlfile = NA, name.pattern = "%NAME%__%TUBE%", flat.table = FALSE, 
     omit.ntp = TRUE)

Arguments

rdmlfile

RDML file with qPCR data.

name.pattern

sample name pattern (see 'Details').

flat.table

logical. If TRUE, a result table is not spited by PCR targets and samples types.

omit.ntp

logical. If TRUE, samples with 'ntp' type (mark for empty sample in data files generated by LightCycler 96 software) are omitted.

Value

• A list of data.frame (or one data.frame), suitable for analysis with qpcR package. Also output list contains information about dilutions (quantity or concentration) if available.

code

name.pattern = "%NAME%__%TUBE%"

cr

Possible keys in name.pattern

• %NAME%- name of the sample inputted in the qPCR software (ex.: "Sample 1")

%ID% - tube ID (ex.: "23") %TUBE% - tube name (ex.: "B11") %TARGET% - PCR target (ex.: "GAPDH") %TYPE% - type of the sample (ex.: "unkn")

Details

ATTENTION: Although the format RDML claimed as data exchange format, the specific implementation of the format at devices from different manufacturers differ significantly. Currently this function is checked against RDML data from devices: Bio-Rad CFX96, Roche LightCycler 96 and Applied Biosystems StepOne. This function has been designed to import qPCR data from instruments that support RDML v1.1 format export. Output from this function can be easily used with qpcR package. There are two general variants of output for this function:

• simplified (flat.table = TRUE). Represents three elementslist:vectorof named dilutions -- 'Dilutions',data.framewith qPCR fluorescence data ofallsamples -- 'qPCR' anddata.framewith melting fluorescence data ofallsamples -- 'Melt'.

splitted (flat.table = FALSE). Represents three elements list: vector of named dilutions -- 'Dilutions', list of data.frames which contain qPCR fluorescence data of samples spited by targets and then by types -- 'qPCR' and list of data.frames which contain melting fluorescence data of samples spited by targets and then by types -- 'Melt'

References

RDML format http://www.rdml.org/ qpcR package http://cran.r-project.org/web/packages/qpcR/index.html

chipPCR package: http://cran.r-project.org/web/packages/chipPCR/index.html

Ritz, C., Spiess, A.-N., 2008. qpcR: an R package for sigmoidal model selection in quantitative real-time polymerase chain reaction analysis. Bioinformatics 24, 1549--1551. doi:10.1093/bioinformatics/btn227

Spiess, A.-N., Feig, C., Ritz, C., 2008. Highly accurate sigmoidal fitting of real-time PCR data by introducing a parameter for asymmetry. BMC Bioinformatics 9, 221. doi:10.1186/1471-2105-9-221

Bustin, S.A., Benes, V., Garson, J.A., Hellemans, J., Huggett, J., Kubista, M., Mueller, R., Nolan, T., Pfaffl, M.W., Shipley, G.L., Vandesompele, J., Wittwer, C.T., 2009. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611--622. doi:10.1373/clinchem.2008.112797

Lefever, S., Hellemans, J., Pattyn, F., Przybylski, D.R., Taylor, C., Geurts, R., Untergasser, A., Vandesompele, J., RDML consortium, 2009. RDML: structured language and reporting guidelines for real-time quantitative PCR data. Nucleic Acids Res. 37, 2065--2069. doi:10.1093/nar/gkp056

Examples

## EXAMPLE 1:
## internal dataset lc96_bACTXY.rdml (in 'data' directory)
## generated by Roche LightCycler 96. Contains qPCR data
## with four targets and two types.
## Import with default settings.
PATH <- path.package("RDML")
filename <- paste(PATH, "/extdata/", "lc96_bACTXY.rdml", sep ="")
lc96 <- RDML(filename)

## Show targets names
names(lc96$qPCR)
## Show types of the samples for target 'FAM@bACT'
names(lc96$qPCR[["FAM@bACT"]])

## Show dilutions for dye - FAM
lc96$Dilutions$FAM
COPIES <- unique(lc96$Dilutions$FAM["quant",])
## Define calibration curves (type of the samples - 'std').
## No replicates.
library(qpcR)
CAL <- modlist(lc96$qPCR[["FAM@bACT"]]$std,
               fluo = c(2, 4, 6, 8, 10))
## Define samples to predict (first two samples with the type - 'unkn').
PRED <- modlist(lc96$qPCR[["FAM@bACT"]]$unkn[1:5],
                fluo = grep("^S", names(lc96$qPCR[["FAM@bACT"]]$unkn)[1:2]))
## Conduct quantification.
calib(refcurve = CAL, predcurve = PRED, thresh = "cpD2",
      dil = COPIES)
## EXAMPLE 2:
## internal dataset lc96_bACTXY.rdml (in 'data' directory)
## generated by Roche LightCycler 96. Contains qPCR data
## with four targets and two types.
## Import with default settings.
library(chipPCR)                        
PATH <- path.package("RDML")
filename <- paste(PATH, "/extdata/", "lc96_bACTXY.rdml", sep ="")
lc96 <- RDML(filename)

## Compactly display the structure of the lc96 object
str(lc96)
## Fetch cycle dependent fluorescence for HEX chanel
tmp <- lc96[["qPCR"]][["Hex@X"]][["std"]]
## Fetch vector of dillutions for HEX chanel
dilution <- as.vector(lc96[["Dilutions"]][["Hex"]])

## Use plotCurves function from the chiPCR package to 
## get an overview of the amplification curves
plotCurves(tmp[, 1], tmp[, -1])
par(mfrow = c(1,1))
## Use inder function from the chiPCR package to 
## calculate the Cq (second derivative maximum, SDM)
SDMout <- sapply(2L:ncol(tmp), function(i) {
  SDM <- summary(inder(tmp[, 1], tmp[, i]), print = FALSE)[2]
})

## Use the effcalc function from the chipPCR package and 
## plot the results for the calculation of the amplification
## efficiency analysis.
plot(effcalc(dilution, SDMout), CI = TRUE)
## EXAMPLE 3:
## internal dataset BioRad_qPCR_melt.rdml (in 'data' directory)
## generated by Bio-Rad CFX96. Contains qPCR and melting data.
## Import without splitting by targets/types and with
## custom name pattern.
PATH <- path.package("RDML")
filename <- paste(PATH, "/extdata/", "BioRad_qPCR_melt.rdml", sep ="")
cfx96 <- RDML(filename, flat.table=TRUE,
                        name.pattern = "%TUBE%_%NAME%_%TYPE%_%TARGET%")
## Use plotCurves function from the chiPCR package to 
## get an overview of the amplification curves
library(chipPCR)                        
plotCurves(cfx96$qPCR[, 1], cfx96$qPCR[, -1], type = "l")

## Show some generated names for samples.
names(cfx96$Melt[2L:5])
## Select index numbers of the columns that contain
## samples with dye 'EvaGreen' and have type 'pos'.
cols <- grep("pos_EvaGreen$", names(cfx96$Melt))
## Conduct melting curve analysis.
library(qpcR)
invisible(meltcurve(cfx96$Melt, fluos = cols,
          temps = rep(1, length(cols))))