TargetExperiment-class: TargetExperiment 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 Targeted Sequencing Experiment in R. Targeted Sequencing Experiments are characterized by a 'bed file' that contains the specification of the explored 'features' as a 'panel'. This features could be amplicons, exons, transcripts, among others. In general each feature is associated to one gene. A gene could be related to many features. This class allows the representation and quality control of a Targeted Sequencing Experiment.

scanBamP: ScanBamParam containing the information to scan the BAM file.
pileupP: PileupParam containing the information to build the pileup.
bedFile: GRanges object that models the bed file.
bamFile: BamFile object that is a reference to the BAM file.
fastaFile: FaFile object that is a reference to the reference sequence.
featurePanel: GRanges object that models the feature panel and related statistics.
genePanel: GRanges object that models the analyzed panel and related statistics at a gene level.
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'.

Model Targeted Sequencing Experiments in R.
Obtain coverage and read counts per sequenced feature.
Evaluate the performance of a targeted sequencing experiment using coverage/read counts information.
Detect in early stage sequencing or library preparation errors.
Explore read profiles for particular features or genomic regions.
Explore any kind of experiment in which 'feature' definition is possible for several genes. E.g RNA-seq experiments in which transcripts could be the 'features'.
Report quality control results.

TargetExperiment S4 class includes the following functions:

pileupCounts: calculate pileup statistics for the BAM file
buildFeaturePanel: build and model a feature panel as a GRanges object and compute read statistics
summarizePanel: summarize the feature panel to a gene panel and compute read statistics
initialize: constructor of TargetExperiment to generate the feature and gene panels starting from an alignment BAM file and the bed file
getBedFile, getBamFile, getFeaturePanel, getGenePanel, getAttribute, getFeature, getScanBamP, getPileupP: return the respective TargetExperiment slot
setAttribute,setFeature, setScanBamP, setPileupP: set the respective TargetExperiment slots
show: generic output of the object
print: generic output of the object
summary: print statistics summary for the set attribute
freqTable: build a frequency table of the attribute occurrence in user configured intervals
plot: plot a summarized view of the feature panel performance
plotAttrExpl: plot the density and distribution of the attribute
plotFeatPerform: plot the sequencing performance for each feature and/or gene
plotFeature: plot the reads profile for a particular feature
plotGeneAttrPerFeat: plot the explored attribute for each feature of a particular gene
plotNtdPercentages: plot nucleotide percentages for each position of a particular feature
plotRegion: plot the reads profile for a particular genomic region
readFrequencies: calculate frequencies of reads fall in and out of targeted regions
plotInOutFeatures: plot frequencies of reads fall in and out of targeted regions
biasExploration: plot attribute distributions along groups of bias sources
plotMetaDataExpl: plot density and box plots or frequency bar plot of metadata columns
addStatSummSheet: internal function to add the first sheet of xlsx reports
buildReport: build the experiment report as an xlsx file

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 TargetExperiment: TargetExperiment, ampliPanel2, ampliPanel, initialize, myCounts

## Defining bam file, bed file and fasta file names and paths
bamFile<-system.file("extdata", "mybam.bam", package="TarSeqQC", 
    mustWork=TRUE)
bedFile<-system.file("extdata", "mybed.bed", package="TarSeqQC",
    mustWork=TRUE)
fastaFile<-system.file("extdata", "myfasta.fa", package="TarSeqQC", 
    mustWork=TRUE)

## Creating a TargetExperiment object

# Defining feature parameter
feature<-"amplicon"
# Defining attribute parameter
attribute<-"coverage"
ampliPanel<-TargetExperiment(bedFile, bamFile, fastaFile, attribute=attribute,
    feature=feature)

## Alternative object creation
# Creating the TargetExperiment object
ampliPanel<-TargetExperiment(bedFile, bamFile, fastaFile)
# Set feature slot value
setFeature(ampliPanel)<-"amplicon"
# Set attribute slot value
setAttribute(ampliPanel)<-"coverage"
# Set pileupP slot value in order to set the maximum depth at 1000
setPileupP(ampliPanel)<-PileupParam(max_depth=1000)
# Set the featurePanel slot but now using the new pileupP definition
setFeaturePanel(ampliPanel)<-buildFeaturePanel(ampliPanel)
## Early exploration
# show/print
ampliPanel
# summary
summary(ampliPanel)
# summary at feature level
summaryFeatureLev(ampliPanel)
# summary at gene level
summaryGeneLev(ampliPanel)
# attribute boxplot and density plot exploration
g<-plotAttrExpl(ampliPanel,level="feature",join=TRUE, log=FALSE, color="blue")
if(interactive()){
x11(type="cairo");g
}
# explore amplicon length distribution
g<-plotMetaDataExpl(ampliPanel, "length", log=FALSE, join=FALSE, color=
"blueviolet")
if(interactive()){
g
}
# explore gene's relative frequencies
g<-plotMetaDataExpl(ampliPanel, "gene", abs=FALSE)
if(interactive()){
g
}
## Deep exploration and Quality Control
myfrequencies<-readFrequencies(ampliPanel)
g<-plotInOutFeatures(readFrequencies(ampliPanel))
if(interactive()){
g
}
# definition of the interval extreme values
attributeThres<-c(0,1,50,200,500, Inf)
# plot panel overview
g<-plot(ampliPanel, attributeThres, chrLabels =TRUE)
if(interactive()){
x11(type="cairo");g
}
# plot panel overview in a feature performance plot
g<-plotFeatPerform(ampliPanel, attributeThres, complete=TRUE, log=FALSE, 
featureLabs=TRUE, sepChr=TRUE, legend=TRUE)
if(interactive()){
g
}
# explore possible attribute bias
g<-biasExploration(ampliPanel, source="gc", dens=TRUE)
if(interactive()){
x11(type="cairo");g
}
## Controlling low counts features
# Do a frequency table for the attribute intervals
summaryIntervals(ampliPanel, attributeThres)
#plotting attribute intervals
g<-plotAttrPerform(ampliPanel)
if(interactive()){
g
}
# getting low counts features at gene level
getLowCtsFeatures(ampliPanel, level="gene", threshold=50)
# getting low counts features at feature level
getLowCtsFeatures(ampliPanel, level="feature", threshold=50)
# exploring amplicon attribute values for a particular gene
g<-plotGeneAttrPerFeat(ampliPanel, geneID="gene4")
# adjust text size
g<-g+theme(title=element_text(size=16), axis.title=element_text(size=16),
legend.text=element_text(size=14))
if(interactive()){
g
}
##Obtain the pileup matrix for the first amplicon
bed<-getBedFile(ampliPanel)[1]
## extracting the pileup matrix
myCounts<-pileupCounts(bed, bamFile, fastaFile)
head(myCounts)
# getting and exploring a sequenced region of a particular gene
getRegion(ampliPanel, level="gene", ID="gene7", collapse=FALSE)
# plot a particular genomic region
g<-plotRegion(ampliPanel,region=c(4500,6800), seqname="chr10", SNPs=TRUE,  
xlab="", title="gene7 amplicons",size=0.5)
if(interactive()){
x11(type="cairo");g
}
# exploring the read count profile for a particular amplicon
g<-plotFeature(ampliPanel, featureID="AMPL20")
if(interactive()){
x11(type="cairo");g
}
# exploring the nucleotide percentages compositions of the read counts for a 
# particular amplicon
g<-plotNtdPercentage(ampliPanel,featureID="AMPL20")
if(interactive()){
g
}
## Building the XLSX report
imageFile<-system.file("extdata", "plot.pdf", package="TarSeqQC",
    mustWork=TRUE)
buildReport(ampliPanel, attributeThres, imageFile ,file="Results.xlsx")