Make oncoPrint
Make oncoPrint
oncoPrint(mat, get_type = default_get_type, alter_fun, alter_fun_is_vectorized = NULL, col = NULL, top_annotation = HeatmapAnnotation(cbar = anno_oncoprint_barplot()), right_annotation = rowAnnotation(rbar = anno_oncoprint_barplot()), left_annotation = NULL, bottom_annotation = NULL, show_pct = TRUE, pct_gp = gpar(fontsize = 10), pct_digits = 0, pct_side = "left", row_labels = NULL, show_row_names = TRUE, row_names_side = "right", row_names_gp = pct_gp, row_split = NULL, column_labels = NULL, column_names_gp = gpar(fontsize = 10), column_split = NULL, row_order = NULL, column_order = NULL, cluster_rows = FALSE, cluster_columns = FALSE, remove_empty_columns = FALSE, remove_empty_rows = FALSE, show_column_names = FALSE, heatmap_legend_param = NULL, ...)
mat |
The value should be a character matrix which encodes mulitple alterations or a list of matrices for which every matrix contains binary value representing whether the alteration is present or absent. When the value is a list, the names of the list represent alteration types. You can use |
get_type |
If different alterations are encoded in the matrix as complex strings, this self-defined function determines how to extract them. It only works when |
alter_fun |
A single function or a list of functions which defines how to add graphics for different alterations. You can use |
alter_fun_is_vectorized |
Whether |
col |
A vector of color for which names correspond to alteration types. |
top_annotation |
Annotation put on top of the oncoPrint. By default it is barplot which shows the number of genes with a certain alteration in each sample. |
right_annotation |
Annotation put on the right of the oncoPrint. By default it is barplot which shows the number of samples with a certain alteration in each gene. |
left_annotation |
Annotation put on the left of the oncoPrint. |
bottom_annotation |
Annotation put at the bottom of the oncoPrint. |
show_pct |
whether show percent values on the left of the oncoprint? |
pct_gp |
Graphic paramters for percent values |
pct_digits |
Digits for the percent values. |
pct_side |
Side of the percent values to the oncoPrint. This argument is currently disabled. |
row_labels |
Labels as the row names of the oncoPrint. |
show_row_names |
Whether show row names? |
row_names_side |
Side of the row names to the oncoPrint. This argument is currently disabled. |
row_names_gp |
Graphic parameters for the row names. |
row_split |
Pass to |
column_labels |
Pass to |
column_names_gp |
Pass to |
column_split |
Pass to |
row_order |
Order of rows. By default rows are sorted by the number of occurence of the alterations. |
cluster_rows |
If it is set, it must be a dendrogram/hclust object. |
cluster_columns |
If it is set, it must be a dendrogram/hclust object. |
column_order |
Order of columns. By default the columns are sorted to show the mutual exclusivity of alterations. |
remove_empty_columns |
If there is no alteration in some samples, whether remove them on the oncoPrint? |
remove_empty_rows |
If there is no alteration in some samples, whether remove them on the oncoPrint? |
show_column_names |
Whether show column names? |
heatmap_legend_param |
pass to |
... |
Pass to |
The 'memo sort' method is from https://gist.github.com/armish/564a65ab874a770e2c26 . Thanks to B. Arman Aksoy for contributing the code.
https://jokergoo.github.io/ComplexHeatmap-reference/book/oncoprint.html gives details for configuring a oncoPrint.
A Heatmap-class
object which means you can add other heatmaps or annotations to it.
Zuguang Gu <z.gu@dkfz.de>
# There is no example NULL
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