A function to plot aggregated signals over sets of GRanges

This function takes one or several RleList genomic tracks (e.g. imported by rtraklayer::import(..., as = 'Rle')) and one or several GRanges objects. It computes coverage of the GRanges by the genomic tracks and returns an aggregate coverage plot.

plotAggregateCoverage(x, ...)

# S3 method for CompressedRleList
plotAggregateCoverage(x, granges, ...)

# S3 method for SimpleRleList
plotAggregateCoverage(
  x,
  granges,
  colors = NULL,
  xlab = "Center of elements",
  ylab = "Score",
  xlim = NULL,
  ylim = NULL,
  quartiles = c(0.025, 0.975),
  verbose = FALSE,
  bin = 1,
  plot_central = TRUE,
  run_in_parallel = FALSE,
  split_by_granges = FALSE,
  norm = "none",
  ...
)

# S3 method for list
plotAggregateCoverage(
  x,
  granges,
  colors = NULL,
  xlab = "Center of elements",
  ylab = "Score",
  xlim = NULL,
  ylim = NULL,
  quartiles = c(0.025, 0.975),
  verbose = FALSE,
  bin = 1,
  plot_central = TRUE,
  split_by_granges = TRUE,
  split_by_track = FALSE,
  free_scales = FALSE,
  run_in_parallel = FALSE,
  norm = "none",
  ...
)

Arguments

x	a single signal track (CompressedRleList or SimpleRleList class), or several signal tracks (SimpleRleList or CompressedRleList class) grouped in a named list
...	additional parameters
granges	a GRanges object or a named list of GRanges
colors	a vector of colors
xlab	x axis label
ylab	y axis label
xlim	y axis limits
ylim	y axis limits
quartiles	Which quantiles to use to determine y scale automatically?
verbose	Boolean
bin	Integer Width of the window to use to smooth values by zoo::rollMean
plot_central	Boolean Draw a vertical line at 0
run_in_parallel	Boolean Should the plots be computed in parallel using mclapply?
split_by_granges	Boolean Facet plots over the sets of GRanges
norm	character Should the signal be normalized ('none', 'zscore' or 'log2')?
split_by_track	Boolean Facet plots by the sets of signal tracks
free_scales	Boolean Should each facet have independent y-axis scales?

Value

An aggregate coverage plot.

Methods (by class)

• CompressedRleList: S3 method for CompressedRleList

• SimpleRleList: S3 method for SimpleRleList

• list: S3 method for list

Examples

data(ce11_ATACseq)
data(ce11_WW_10bp)
data(ce11_proms)

p1 <- plotAggregateCoverage(
    ce11_ATACseq, 
    resize(ce11_proms[1:100], fix = 'center', width = 1000)
)
p1

proms <- resize(ce11_proms[1:100], fix = 'center', width = 400)
p2 <- plotAggregateCoverage(
    ce11_ATACseq, 
    list(
        'Ubiq & Germline promoters' = 
            proms[proms$which.tissues %in% c('Ubiq.', 'Germline')],
        'Other promoters' = 
            proms[!(proms$which.tissues %in% c('Ubiq.', 'Germline'))]
    )
)
p2

p3 <- plotAggregateCoverage(
    list(
        'atac' = ce11_ATACseq, 
        'WW_10bp' = ce11_WW_10bp
    ), 
    proms,
    norm = 'zscore'
)
p3

p4 <- plotAggregateCoverage(
    list(
        'ATAC-seq' = ce11_ATACseq, 
        'WW 10-bp periodicity' = ce11_WW_10bp
    ), 
    list(
        'Ubiq & Germline promoters' = 
            proms[proms$which.tissues %in% c('Ubiq.', 'Germline')],
        'Other promoters' = 
            proms[!(proms$which.tissues %in% c('Ubiq.', 'Germline'))]
    ), 
    norm = 'zscore'
)
p4

p5 <- plotAggregateCoverage(
    list(
        'ATAC-seq' = ce11_ATACseq, 
        'WW 10-bp periodicity' = ce11_WW_10bp
    ), 
    list(
        'Ubiq & Germline promoters' = 
            proms[proms$which.tissues %in% c('Ubiq.', 'Germline')],
        'Other promoters' = 
            proms[!(proms$which.tissues %in% c('Ubiq.', 'Germline'))]
    ), 
    split_by_granges = FALSE,
    split_by_track = TRUE,
    norm = 'zscore'
)
p5