Plot the distribution of cluster sizes

Description

Produce an histogram of cluster sizes

Usage

plotSizeDistribution( clusters, showCov = FALSE, ... )

Arguments

Value

Called for its effect, returns a histogram.

Author(s)

Federico Comoglio

See Also

getClusters

Examples

require(BSgenome.Hsapiens.UCSC.hg19)

data( model, package = "wavClusteR" ) 

filename <- system.file( "extdata", "example.bam", package = "wavClusteR" )
example <- readSortedBam( filename = filename )
countTable <- getAllSub( example, minCov = 10, cores = 1 )
highConfSub <- getHighConfSub( countTable, supportStart = 0.2, supportEnd = 0.7, substitution = "TC" )
coverage <- coverage( example )
clusters <- getClusters( highConfSub = highConfSub, 
                         coverage = coverage, 
                         sortedBam = example, 
	                 method = 'mrn', 
	                 cores = 1, 
	                 threshold = 2 ) 

fclusters <- filterClusters( clusters = clusters, 
		             highConfSub = highConfSub, 
        		     coverage = coverage,
			     model = model, 
			     genome = Hsapiens, 
		             refBase = 'T', 
		             minWidth = 12 )
plotSizeDistribution(fclusters, breaks = 30, col = 'skyblue2')

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 'license()' or 'licence()' for distribution details.

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Type 'demo()' for some demos, 'help()' for on-line help, or
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Type 'q()' to quit R.

> library(wavClusteR)
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
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/wavClusteR/plotSizeDistribution.Rd_%03d_medium.png", width=480, height=480)
> ### Name: plotSizeDistribution
> ### Title: Plot the distribution of cluster sizes
> ### Aliases: plotSizeDistribution
> ### Keywords: graphics postprocessing
> 
> ### ** Examples
> 
> 
> require(BSgenome.Hsapiens.UCSC.hg19)
Loading required package: BSgenome.Hsapiens.UCSC.hg19
Loading required package: BSgenome
Loading required package: rtracklayer
> 
> data( model, package = "wavClusteR" ) 
> 
> filename <- system.file( "extdata", "example.bam", package = "wavClusteR" )
> example <- readSortedBam( filename = filename )
> countTable <- getAllSub( example, minCov = 10, cores = 1 )
Loading required package: doMC
Loading required package: foreach
Loading required package: iterators
Considering substitutions, n = 497, processing in 1 chunks
   chunk #: 1
   considering the + strand
Computing local coverage at substitutions...
   considering the - strand
Computing local coverage at substitutions...
> highConfSub <- getHighConfSub( countTable, supportStart = 0.2, supportEnd = 0.7, substitution = "TC" )
> coverage <- coverage( example )
> clusters <- getClusters( highConfSub = highConfSub, 
+                          coverage = coverage, 
+                          sortedBam = example, 
+ 	                 method = 'mrn', 
+ 	                 cores = 1, 
+ 	                 threshold = 2 ) 
Computing start/end read positions
Number of chromosomes exhibiting high confidence transitions: 1
...Processing = chrX
> 
> fclusters <- filterClusters( clusters = clusters, 
+ 		             highConfSub = highConfSub, 
+         		     coverage = coverage,
+ 			     model = model, 
+ 			     genome = Hsapiens, 
+ 		             refBase = 'T', 
+ 		             minWidth = 12 )
Computing log odds...
Refining cluster sizes...
Combining clusters...
Quantifying transitions within clusters...
Computing statistics...
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Consolidating results...
> plotSizeDistribution(fclusters, breaks = 30, col = 'skyblue2')
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>