A function to compute (and plot) a Vmat

The plotVmat.GRanges() method computes and normalizes a Vmat before passing it to plotVmat.Vmat() method.

# S3 method for GRanges
plotVmat(
  x,
  granges,
  xlims = c(-250, 250),
  ylims = c(50, 300),
  normFun = "",
  s = 0.95,
  roll = 3,
  cores = 1,
  return_Vmat = FALSE,
  verbose = 1,
  ...
)

Arguments

x

GRanges, paired-end fragments

granges

GRanges, loci to map the fragments onto

xlims

x limits of the computed Vmat

ylims

y limits of the computed Vmat

normFun

character. A Vmat should be scaled either by:

• 'libdepth+nloci', e.g. the library depth and the number of loci used to compute the Vmat;

• zscore, if relative patterns of fragment density are more important than density per se;

• Alternatively, the Vmat can be scaled to a chosen quantile ('quantile') or to the max Vmat value ('max').

s

A float indicating which quantile to use if 'quantile' normalization is chosen

roll

integer, to use as the window to smooth the Vmat rows by rolling mean.

cores

Integer, number of threads to parallelize fragments subsetting

return_Vmat

Boolean, should the function return the computed Vmat rather than the plot?

verbose

Boolean

...

additional parameters

Value

A Vmat ggplot

Examples

data(bam_test)
data(ce11_proms)
V <- plotVmat(
    bam_test,
    ce11_proms,
    normFun = 'libdepth+nloci', 
    roll = 5
)
#> Computing V-mat
#> Normalizing the matrix
#> Computing raw library depth
#> Dividing Vmat by its number of loci
#> Smoothing the matrix