TargetExperimentList-class: TargetExperimentList S4 class implementation in R
In TarSeqQC: TARgeted SEQuencing Experiment Quality Control

Description Slots Features Functions Author(s) See Also Examples

This S4 class represents a collection of Targeted Sequencing Experiments in R. All these experiments are characterized by a 'bed file' containing the specification of the explored 'features', as a 'feature panel'. These features could be amplicons, exons, transcripts, among others. In general each feature is associated to one gene but a gene could be related to many features. This class allows the representation and quality control of a set of Targeted Sequencing Experiment made over the same or different subjects but using always the same bed file'.

bedFile: GRanges object that models the bed file.
panels: GRanges object containing the feature/gene panels.
attribute: character indicates which attribute, 'coverage' or 'medianCounts' will be used to the analysis.
feature: character indicates the name of the analyzed features. E.g 'amplicon', 'exon', 'transcript', 'gene'.

Model sets of targeted sequencing experiments in R.
Evaluate the performance of the targeted sequencing technique across several experiments using coverage/read counts information.
Detect in early stage sequencing or library preparation errors.
Report quality control results.

TargetExperimentList S4 class includes the following functions:

initialize: constructor of TargetExperimentList to generate the feature panel starting from at least two TargetExperiment objects
getBedFile, getPanels, getAttribute, getFeature: return the respective TargetExperimentList slots
setFeature: set the respective TargetExperimentList slot
show: generic output of the object
print: generic output of the object
summary: print statistics summary for the set attribute
plot: plot a summarized view of the attribute values achieved by each feature in each sample
plotGlobalAttrExpl: plot the attribute distribution for each feature
plotAttrExpl: plot the attribute distribution in each panel
plotpoolPerformance: plot the attribute distribution in each or pool

Gabriela A. Merino gmerino@bdmg.com.ar, Cristobal Fresno cfresno@bdmg.com.ar, Yanina Murua ymurua@leloir.org.ar, Andrea S. Llera allera@leloir.org.ar and Elmer A. Fernandez efernandez@bdmg.com.ar

Rsamtools

Other TargetExperimentList: TargetExperimentList, initialize,TargetExperimentList-method, object

# Defining the set of TargetExperiment objects
data(ampliPanel, package="TarSeqQC")
data(ampliPanel2, package="TarSeqQC")
ampliList<-list(ampliPanel, ampliPanel2)
# Defining feature parameter
feature<-"amplicon"
# Defining attribute parameter
attribute<-"coverage"
##Calling the constructor
object<-TargetExperimentList(TEList=ampliList, attribute=attribute,
    feature=feature)
setFeature(object)<-"amplicon"
## load the example dataset
data(TEList, package="TarSeqQC")
## Early exploration
# show/print
TEList
# summary
summary(TEList)
## Controlling low counts features
# Definition of the interval extreme values
attributeThres<-c(0,1,50,200,500, Inf)
# Do a frequency table for the attribute intervals
summaryIntervals(TEList, attributeThres)
# getting low counts features at gene level
getLowCtsFeatures(TEList, level="gene", threshold=50)
# exploring panel performance along several samples
g<-plot(TEList, attributeThres=attributeThres, featureLabs =TRUE)
if(interactive()){
g
}
g<-plotGlobalAttrExpl(TEList,log=FALSE)
# x11(type="cairo")
if(interactive()){
g
}
g<-plotPoolPerformance(TEList,log=FALSE)
if(interactive()){
g
}