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 = list(title = "Alterations"),
    ...)

Arguments

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 unify_mat_list to make all matrix having same row names and column names.
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 mat is a matrix. The default value is default_get_type.
alter_fun	A single function or a list of functions which defines how to add graphics for different alterations.
alter_fun_is_vectorized	Whether alter_fun is implemented vectorized. Internally the function will guess.
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 Heatmap.
column_labels	Pass to Heatmap.
column_names_gp	Pass to Heatmap.
column_split	Pass to Heatmap.
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 Heatmap.
...	Pass to Heatmap.

Details

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.

Value

A Heatmap-class object which means you can add other heatmaps or annotations to it.

Examples

# There is no example
NULL
#> NULL