plot_categorical {annotatr} | R Documentation |
Given a GRanges
of annotated regions from annotate_regions()
, visualize the the distribution of categorical data fill
in categorical data x
. A bar representing the distribution of all fill
in x
will be added according to the contents of fill
. This is the distribution over all values of x
. Additionally, when annotated_random
is not missing, a "Random Regions" bar shows the distribution of random regions over fill
.
plot_categorical( annotated_regions, annotated_random, x, fill = NULL, x_order = NULL, fill_order = NULL, position = "stack", plot_title, legend_title, x_label, y_label, quiet = FALSE )
annotated_regions |
The |
annotated_random |
The |
x |
One of 'annot.type' or a categorical data column, indicating whether annotation classes or data classes will appear on the x-axis. |
fill |
One of 'annot.type', a categorical data column, or |
x_order |
A character vector that subsets and orders the x classes. Default |
fill_order |
A character vector that subsets and orders the fill classes. Default |
position |
A string which has the same possible values as in |
plot_title |
A string used for the title of the plot. If missing, no title is displayed. |
legend_title |
A string used for the legend title to describe fills (if fill is not |
x_label |
A string used for the x-axis label. If missing, corresponding variable name used. |
y_label |
A string used for the y-axis label. If missing, corresponding variable name used. |
quiet |
Print progress messages (FALSE) or not (TRUE). |
For example, if a differentially methylated region has the categorical label hyper, and is annotated to a promoter, a 5UTR, two exons, and an intron. Each annotation will appear in the All bar once. Likewise for the hyper bar if the differential methylation status is chosen as x
with annot.type
chosen as fill
.
A ggplot
object which can be viewed by calling it, or saved with ggplot2::ggsave
.
# Get premade CpG annotations data('annotations', package = 'annotatr') dm_file = system.file('extdata', 'IDH2mut_v_NBM_multi_data_chr9.txt.gz', package = 'annotatr') extraCols = c(diff_meth = 'numeric', mu1 = 'numeric', mu0 = 'numeric') dm_regions = read_regions(con = dm_file, extraCols = extraCols, genome = 'hg19', rename_score = 'pval', rename_name = 'DM_status', format = 'bed') dm_regions = dm_regions[1:1000] dm_annots = annotate_regions( regions = dm_regions, annotations = annotations, ignore.strand = TRUE) dm_order = c( 'hyper', 'hypo') cpg_order = c( 'hg19_cpg_islands', 'hg19_cpg_shores', 'hg19_cpg_shelves', 'hg19_cpg_inter') dm_vn = plot_categorical( annotated_regions = dm_annots, x = 'DM_status', fill = 'annot.type', x_order = dm_order, fill_order = cpg_order, position = 'fill', legend_title = 'knownGene Annotations', x_label = 'DM status', y_label = 'Proportion') # Create randomized regions dm_rnd_regions = randomize_regions(regions = dm_regions) dm_rnd_annots = annotate_regions( regions = dm_rnd_regions, annotations = annotations, ignore.strand = TRUE) dm_vn_rnd = plot_categorical( annotated_regions = dm_annots, annotated_random = dm_rnd_annots, x = 'DM_status', fill = 'annot.type', x_order = dm_order, fill_order = cpg_order, position = 'fill', legend_title = 'knownGene Annotations', x_label = 'DM status', y_label = 'Proportion')