Function to build a feature panel based on specific genomic regions.

Description

buildFeaturePanel builds panel slots of a TargetExperiment object. Input can be a bam file or a pileup matrix. If the bed file contains a high number of amplicons, the bam file as input is recommended in order to diminish memory requirements. The resulting object is a GRanges instance having panel and counts/coverage information.

Usage

buildFeaturePanel(object, BPPARAM = bpparam())

## S4 method for signature 'TargetExperiment'
buildFeaturePanel(object, BPPARAM = bpparam())

Arguments

Value

GRanges object.

Note

see full example in TargetExperiment-class

Author(s)

Gabriela A. Merino gmerino@bdmg.com.ar, Cristobal Fresno cfresno@bdmg.com.ar and Elmer A. Fernandez efernandez@bdmg.com.ar

Examples

if (interactive()) {
    ## loading TargetExperiment object
    data(ampliPanel, package="TarSeqQC")
    ## Defining bam file, bed file and fasta file names and paths
    setBamFile(ampliPanel)<-system.file("extdata", "mybam.bam",
        package="TarSeqQC", mustWork=TRUE)
    setFastaFile(ampliPanel)<-system.file("extdata", "myfasta.fa",
        package="TarSeqQC", mustWork=TRUE)
    myFeaturePanel<-buildFeaturePanel(ampliPanel)
}

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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Type 'demo()' for some demos, 'help()' for on-line help, or
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> library(TarSeqQC)
Loading required package: GenomicRanges
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Rsamtools
Loading required package: Biostrings
Loading required package: XVector
Loading required package: ggplot2
Loading required package: plyr

Attaching package: 'plyr'

The following object is masked from 'package:XVector':

    compact

The following object is masked from 'package:IRanges':

    desc

The following object is masked from 'package:S4Vectors':

    rename

Loading required package: openxlsx
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/TarSeqQC/TargetExperiment-buildFeaturePanel.Rd_%03d_medium.png", width=480, height=480)
> ### Name: buildFeaturePanel
> ### Title: Function to build a feature panel based on specific genomic
> ###   regions.
> ### Aliases: buildFeaturePanel buildFeaturePanel,TargetExperiment-method
> ###   buildFeaturePanel-methods
> 
> ### ** Examples
> 
> #if (interactive()) {
>     ## loading TargetExperiment object
>     data(ampliPanel, package="TarSeqQC")
>     ## Defining bam file, bed file and fasta file names and paths
>     setBamFile(ampliPanel)<-system.file("extdata", "mybam.bam",
+         package="TarSeqQC", mustWork=TRUE)
>     setFastaFile(ampliPanel)<-system.file("extdata", "myfasta.fa",
+         package="TarSeqQC", mustWork=TRUE)
>     myFeaturePanel<-buildFeaturePanel(ampliPanel)
> #}
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>