Usage
oncoPrint(mat, get_type = function(x) x,
alter_fun = alter_fun_list, alter_fun_list = NULL, col,
row_order = oncoprint_row_order(),
column_order = oncoprint_column_order(),
show_column_names = FALSE,
show_pct = TRUE, pct_gp = gpar(), pct_digits = 0,
axis_gp = gpar(fontsize = 8),
show_row_barplot = TRUE,
row_barplot_width = unit(2, "cm"),
remove_empty_columns = FALSE,
heatmap_legend_param = list(title = "Alterations"),
top_annotation = HeatmapAnnotation(column_bar = anno_column_bar,
annotation_height = unit(2, "cm")),
barplot_ignore = NULL,
...)Arguments
mat
a character matrix which encodes mulitple alterations or a list of matrix for which every matrix contains binary value representing the alteration is present or absent. When it is a list, the names represent alteration types. You can use unify_mat_list to make all matrix having same row names and column names. get_type
If different alterations are encoded in the matrix, this self-defined function determines how to extract them. Only work when mat is a matrix.
alter_fun
a single function or a list of functions which define how to add graphics for different alterations. If it is a list, the names of the list should cover all alteration types.
alter_fun_list
deprecated, use alter_run instead.
col
a vector of color for which names correspond to alteration types.
row_order
order of genes. By default it is sorted by frequency of alterations decreasingly. Set it to NULL if you don't want to set the order
column_order
order of samples. By default the order is calculated by the 'memo sort' method which can visualize the mutual exclusivity across genes. Set it to NULL if you don't want to set the order
show_column_names
whether show column names
show_pct
whether show percent values on the left of the oncoprint
pct_gp
graphic paramters for percent row annotation
pct_digits
digits for percent values
axis_gp
graphic paramters for axes
show_row_barplot
whether show barplot annotation on rows
row_barplot_width
width of barplot annotation on rows. It should be a unit object remove_empty_columns
if there is no alteration in that sample, whether remove it on the heatmap
top_annotation
by default the top annotation contains barplots representing frequency of mutations in every sample.
barplot_ignore
alterations that you don't want to put on the barplots.
...
pass to Heatmap, so can set bottom_annotation here.