RDML: R6 class 'RDML' - contains methods to read and overview...
In RDML: Importing Real-Time Thermo Cycler (qPCR) Data from RDML Format Files
Description
Usage
Format
Fields
Methods
Author(s)
References
Examples
Description
This class is a container for RDML format data (Lefever et al.
2009). The data may be further transformed to the appropriate format of the
qpcR (Ritz et al. 2008, Spiess et al. 2008) and chipPCR
(Roediger et al. 2015) 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). The inner structure of
imported data faithfully reflects the structure of RDML file v1.2. All data with
the exception for fluorescence values can be represented as data.frame by
method AsTable. Such possibility of data representation streamlines
sample filtering (by targets, types, etc.) and serves as request for GetFData
method, which extracts fluorescence data for specified samples.
Usage
1
Format
An R6Class generator object.
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.
Methods
- new
creates a new instance of RDML class object (see RDML.new)
- AsTable
represent RDML data as data.frame (see RDML.AsTable)
- GetFData
gets fluorescence data (see RDML.GetFData)
- SetFData
sets fluorescence data (see RDML.SetFData)
- Merge
merges two RDML to one (see MergeRDMLs)
- AsDendrogram
represents structure of RDML object as dendrogram(see
RDML.AsDendrogram)
Author(s)
Konstantin A. Blagodatskikh <k.blag@yandex.ru>, Stefan Roediger
<stefan.roediger@b-tu.de>, Michal Burdukiewicz
<michalburdukiewicz@gmail.com>
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
Roediger S, Burdukiewicz M and Schierack P (2015). chipPCR: an R Package
to Pre-Process Raw Data of Amplification Curves. Bioinformatics first
published online April 24, 2015 doi:10.1093/bioinformatics/btv205
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
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## 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$sample$id]]$description,
react$id$id),
quantity = sample[[react$sample$id]]$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")$sample.type)
## Show template quantities for dye 'FAM' type 'std'#'
## Not run:
COPIES <- filter(tab, target.dyeId == "FAM", sample.type == "std")$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)
## End(Not run)
## Not run:
## 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$sample$id]]$description,
react$id$id),
quantity = sample[[react$sample$id]]$quantity$value)
## Show targets names
unique(tab$target)
## Fetch cycle dependent fluorescence for HEX chanel
tmp <- lc96$GetFData(filter(tab, target == "bACT", sample.type == "std"))
## Fetch vector of dillutions
dilution <- filter(tab, target.dyeId == "FAM", sample.type == "std")$quantity
## Use plotCurves function from the chipPCR package to
## get an overview of the amplification curves
tmp <- as.data.frame(tmp)
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)
## End(Not run)
## Not run:
## 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 <- as.data.frame(cfx96$GetFData(tab))
plotCurves(cfx96.qPCR[,1], cfx96.qPCR[,-1], type = "l")
## Extract all melting data
cfx96.melt <- cfx96$GetFData(tab, dp.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)),
dp.type = "mdp")
## Conduct melting curve analysis.
library(qpcR)
invisible(meltcurve(cols, fluos = 2:ncol(cols),
temps = rep(1, ncol(cols) - 1)))
## End(Not run)
RDML documentation built on June 25, 2019, 5:03 p.m.
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Description Usage Format Fields Methods Author(s) References Examples
Description
This class is a container for RDML format data (Lefever et al.
2009). The data may be further transformed to the appropriate format of the
qpcR (Ritz et al. 2008, Spiess et al. 2008) and chipPCR
(Roediger et al. 2015) 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). The inner structure of
imported data faithfully reflects the structure of RDML file v1.2. All data with
the exception for fluorescence values can be represented as data.frame by
method AsTable. Such possibility of data representation streamlines
sample filtering (by targets, types, etc.) and serves as request for GetFData
method, which extracts fluorescence data for specified samples.
Usage
1 |
Format
An R6Class generator object.
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.
Methods
- new
creates a new instance of
RDMLclass object (see RDML.new)- AsTable
represent RDML data as
data.frame(see RDML.AsTable)- GetFData
gets fluorescence data (see RDML.GetFData)
- SetFData
sets fluorescence data (see RDML.SetFData)
- Merge
merges two
RDMLto one (see MergeRDMLs)- AsDendrogram
represents structure of
RDMLobject as dendrogram(see RDML.AsDendrogram)
Author(s)
Konstantin A. Blagodatskikh <k.blag@yandex.ru>, Stefan Roediger <stefan.roediger@b-tu.de>, Michal Burdukiewicz <michalburdukiewicz@gmail.com>
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
Roediger S, Burdukiewicz M and Schierack P (2015). chipPCR: an R Package to Pre-Process Raw Data of Amplification Curves. Bioinformatics first published online April 24, 2015 doi:10.1093/bioinformatics/btv205
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 | ## 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$sample$id]]$description,
react$id$id),
quantity = sample[[react$sample$id]]$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")$sample.type)
## Show template quantities for dye 'FAM' type 'std'#'
## Not run:
COPIES <- filter(tab, target.dyeId == "FAM", sample.type == "std")$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)
## End(Not run)
## Not run:
## 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$sample$id]]$description,
react$id$id),
quantity = sample[[react$sample$id]]$quantity$value)
## Show targets names
unique(tab$target)
## Fetch cycle dependent fluorescence for HEX chanel
tmp <- lc96$GetFData(filter(tab, target == "bACT", sample.type == "std"))
## Fetch vector of dillutions
dilution <- filter(tab, target.dyeId == "FAM", sample.type == "std")$quantity
## Use plotCurves function from the chipPCR package to
## get an overview of the amplification curves
tmp <- as.data.frame(tmp)
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)
## End(Not run)
## Not run:
## 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 <- as.data.frame(cfx96$GetFData(tab))
plotCurves(cfx96.qPCR[,1], cfx96.qPCR[,-1], type = "l")
## Extract all melting data
cfx96.melt <- cfx96$GetFData(tab, dp.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)),
dp.type = "mdp")
## Conduct melting curve analysis.
library(qpcR)
invisible(meltcurve(cols, fluos = 2:ncol(cols),
temps = rep(1, ncol(cols) - 1)))
## End(Not run)
|
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