Graphs of sample read counts (quality assesment)

Description

Graphs of sample read counts (quality assesment)

Usage

countsPlot(listCounts, ixCounts, log2Bool)

Arguments

Value

A list of pairs and boxplots between the counts data in each data.frame.

Examples

#read the BAM file into a GAlignments object using
#GenomicAlignments::readGAlignments
#the GAlignments object should be similar to ctrlGAlignments
data(ctrlGAlignments)
aln <- ctrlGAlignments

#transform the GAlignments object into a GRanges object (faster processing)
alnGRanges <- readsToStartOrEnd(aln, what="start")

#make a txdb object containing the annotations for the specified species.
#In this case hg19.
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene
#Please make sure that seqnames of txdb correspond to
#the seqnames of the alignment files ("chr" particle)
#if not rename the txdb seqlevels
#renameSeqlevels(txdb, sub("chr", "",seqlevels(txdb)))
#get the flanking region around the promoter of the best expressed CDSs

#get all CDSs by transcript
cds <- GenomicFeatures::cdsBy(txdb,by="tx",use.names=TRUE)

#get all exons by transcript
exonGRanges <- GenomicFeatures::exonsBy(txdb,by="tx",use.names=TRUE)

#get the per transcript relative position of start and end codons
cdsPosTransc <- orfRelativePos(cds, exonGRanges)

#compute the counts on the different features after applying
#the specified shift value on the read start along the transcript
countsData <-
   countShiftReads(
         exonGRanges[names(cdsPosTransc)],
         cdsPosTransc,
         alnGRanges,
         -14
     )

#now make the plots
listCountsPlots <- countsPlot(
   list(countsData[[1]]),
   grep("_counts$", colnames(countsData[[1]])),
   1
)
listCountsPlots

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
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> library(RiboProfiling)
Loading required package: Biostrings
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: XVector
Warning messages:
1: replacing previous import 'BiocGenerics::Position' by 'ggplot2::Position' when loading 'RiboProfiling' 
2: replacing previous import 'ggplot2::Position' by 'BiocGenerics::Position' when loading 'ggbio' 
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/RiboProfiling/countsPlot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: countsPlot
> ### Title: Graphs of sample read counts (quality assesment)
> ### Aliases: countsPlot
> 
> ### ** Examples
> 
> #read the BAM file into a GAlignments object using
> #GenomicAlignments::readGAlignments
> #the GAlignments object should be similar to ctrlGAlignments
> data(ctrlGAlignments)
> aln <- ctrlGAlignments
> 
> #transform the GAlignments object into a GRanges object (faster processing)
> alnGRanges <- readsToStartOrEnd(aln, what="start")
> 
> #make a txdb object containing the annotations for the specified species.
> #In this case hg19.
> txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene
> #Please make sure that seqnames of txdb correspond to
> #the seqnames of the alignment files ("chr" particle)
> #if not rename the txdb seqlevels
> #renameSeqlevels(txdb, sub("chr", "",seqlevels(txdb)))
> #get the flanking region around the promoter of the best expressed CDSs
> 
> #get all CDSs by transcript
> cds <- GenomicFeatures::cdsBy(txdb,by="tx",use.names=TRUE)
> 
> #get all exons by transcript
> exonGRanges <- GenomicFeatures::exonsBy(txdb,by="tx",use.names=TRUE)
> 
> #get the per transcript relative position of start and end codons
> cdsPosTransc <- orfRelativePos(cds, exonGRanges)
> 
> #compute the counts on the different features after applying
> #the specified shift value on the read start along the transcript
> countsData <-
+    countShiftReads(
+          exonGRanges[names(cdsPosTransc)],
+          cdsPosTransc,
+          alnGRanges,
+          -14
+      )
Warning message:
In countShiftReads(exonGRanges[names(cdsPosTransc)], cdsPosTransc,  :
  Param motifSize should be an integer! Accepted values 3, 6 or 9. Default value is 3.

> 
> #now make the plots
> listCountsPlots <- countsPlot(
+    list(countsData[[1]]),
+    grep("_counts$", colnames(countsData[[1]])),
+    1
+ )
> listCountsPlots
[[1]]
[[1]][[1]]


[[2]]

> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>