TargetExperiment-class {TarSeqQC} | R Documentation

## TargetExperiment S4 class implementation in R

### Description

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.

### Slots

`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'.

### Features

1. Model Targeted Sequencing Experiments in R.

2. Obtain coverage and read counts per sequenced feature.

3. Evaluate the performance of a targeted sequencing experiment using coverage/read counts information.

4. Detect in early stage sequencing or library preparation errors.

5. Explore read profiles for particular features or genomic regions.

6. 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'.

7. Report quality control results.

### Functions

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

### Author(s)

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

### See Also

Rsamtools

Other TargetExperiment: `TargetExperiment`, `ampliPanel2`, `ampliPanel`, `initialize`, `myCounts`

### Examples

    ## 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")

[Package TarSeqQC version 1.10.0 Index]