RDML (version 0.8-3)

RDML.class: R6 class RDML -- contains methods to read and overview fluorescence data from RDML v1.1 and v1.2 format files

Description

Main purpose of this class is to work with RDML format data (Lefever et al. 2009) and transform it to the appropriate format of the qpcR (Ritz et al. 2008, Spiess et al. 2008) and chipPCR packages (see RDML.new for import details). Real-time PCR Data Markup Language (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). Inner structure of imported data mimics structure of RDML file v1.2. All data except fluorescence values can be represented as data.frame by method AsTable. Such variant of data representation allows easy samples filtering (by targets, types, etc.) and serves as request for GetFData method -- gets fluorescence data for specified samples.

Usage

RDML

format

Class 'R6ClassGenerator' - attr(*, "name")= chr "RDML_generator"

Fields

Type, structure of data and description of fields can be viewed at RDML v1.2 file description. Names of fields are first level of XML tree.

References

RDML format http://www.rdml.org/ R6 package http://cran.r-project.org/web/packages/R6/index.html 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$new(filename)

tab <- lc96$AsTable(name.pattern = paste(sample[[react$sample]]$description,
                                         react$id),
                    quantity = sample[[react$sample]]$quantity$value)
## Show dyes names
unique(tab$target.dyeId)
## Show types of the samples for dye 'FAM'
library(dplyr)
unique(filter(tab, target.dyeId == "FAM")$sample.type)

## Show template quantities for dye 'FAM' type 'std'#'
COPIES <- filter(tab, target.dyeId == "FAM", sample.type == "std")$quantity
## Define calibration curves (type of the samples - 'std').
## No replicates.
library(qpcR)
CAL <- modlist(lc96$GetFData(filter(tab,
                                    target.dyeId == "FAM",
                                    sample.type == "std")),
               baseline="lin", basecyc=8:15)
## Define samples to predict (first two samples with the type - 'unkn').
PRED <- modlist(lc96$GetFData(filter(tab,
                                    target.dyeId == "FAM",
                                    sample.type == "unkn")),
               baseline="lin", basecyc=8:15)
## Conduct quantification.
calib(refcurve = CAL, predcurve = PRED, thresh = "cpD2",
      dil = COPIES)
## EXAMPLE 2:
## internal dataset lc96_bACTXY.rdml (in 'data' directory)
## generated by Roche LightCycler 96. Contains qPCR data
## with four targets and two types.
## Import with default settings.
library(chipPCR)
PATH <- path.package("RDML")
filename <- paste(PATH, "/extdata/", "lc96_bACTXY.rdml", sep ="")
lc96 <- RDML$new(filename)

tab <- lc96$AsTable(name.pattern = paste(sample[[react$sample]]$description,
                                         react$id),
                    quantity = sample[[react$sample]]$quantity$value)
## Show targets names
unique(tab$target)
## Fetch cycle dependent fluorescence for HEX chanel
tmp <- lc96$GetFData(filter(tab, target == "FAM@bACT", sample.type == "std"))
## Fetch vector of dillutions
dilution <- filter(tab, target.dyeId == "FAM", sample.type == "std")$quantity

## Use plotCurves function from the chipPCR package to
## get an overview of the amplification curves
plotCurves(tmp[,1], tmp[,-1])
par(mfrow = c(1,1))
## Use inder function from the chipPCR 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 with custom name pattern.
PATH <- path.package("RDML")
filename <- paste(PATH, "/extdata/", "BioRad_qPCR_melt.rdml", sep ="")
cfx96 <- RDML$new(filename)
## Use plotCurves function from the chipPCR package to
## get an overview of the amplification curves
library(chipPCR)
## Extract all qPCR data
tab <- cfx96$AsTable()
cfx96.qPCR <- cfx96$GetFData(tab)
plotCurves(cfx96.qPCR[,1], cfx96.qPCR[,-1], type = "l")

## Extract all melting data
cfx96.melt <- cfx96$GetFData(tab, data.type = "mdp")
## Show some generated names for samples.
colnames(cfx96.melt)[2L:5]
## Select columns that contain
## samples with dye 'EvaGreen' and have type 'pos'
## using filtering by names.
cols <- cfx96$GetFData(filter(tab, grepl("pos_EvaGreen$", fdata.name)),
                       data.type = "mdp")
## Conduct melting curve analysis.
library(qpcR)
invisible(meltcurve(cols, fluos = 2:ncol(cols),
          temps = rep(1, ncol(cols) - 1)))