plotAggregateCoverage | R Documentation |
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 class 'CompressedRleList' plotAggregateCoverage(x, granges, ...) ## S3 method for class '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 class '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", ... )
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? |
An aggregate coverage plot.
CompressedRleList
: S3 method for CompressedRleList
SimpleRleList
: S3 method for SimpleRleList
list
: S3 method for list
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
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