plotStatistics: Pairs plot visualization of clusters statistics

In FedericoComoglio/wavClusteR: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data

Description

Graphical representation of cluster statistics, featuring pairwise correlations in the upper panel.

Usage

plotStatistics(clusters, corMethod = 'spearman', lower =
panel.smooth, ...)

Arguments

clusters	GRanges object containing individual clusters as identified by the getClusters function
corMethod	A character defining the correlation coefficient to be computed. See the help page of the cor function for possible options. Default is "spearman". Hence, rank-based Spearman's correlation coefficients are computed
lower	A function compatible with the lower panel argument of the pairs function
...	Additional parameters to be passed to the pairs function

Value

called for its effect

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, 
	                        threshold = 2 ) 

fclusters <- filterClusters( clusters = clusters, 
		             highConfSub = highConfSub, 
        		     coverage = coverage,
			     model = model, 
			     genome = Hsapiens, 
		             refBase = 'T', 
		             minWidth = 12 )
plotStatistics( clusters = fclusters )

FedericoComoglio/wavClusteR documentation built on Oct. 29, 2020, 2:44 p.m.