| ht_clusters {simplifyEnrichment} | R Documentation |
Visualize the similarity matrix and the clustering
ht_clusters(
mat,
cl,
dend = NULL,
col = c("white", "red"),
# arguments that control the word cloud annotation
draw_word_cloud = is_GO_id(rownames(mat)[1]) || !is.null(term),
term = NULL,
min_term = round(nrow(mat)*0.01),
order_by_size = FALSE,
exclude_words = character(0),
max_words = 10,
word_cloud_grob_param = list(),
fontsize_range = c(4, 16),
bg_gp = gpar(fill = "#DDDDDD", col = "#AAAAAA"),
# arguments that control the heatmaps
column_title = NULL,
ht_list = NULL,
use_raster = TRUE,
run_draw = TRUE,
...)
mat |
A similarity matrix. |
cl |
Cluster labels inferred from the similarity matrix, e.g. from |
dend |
Used internally. |
col |
A vector of colors that map from 0 to the 95^th percentile of the similarity values. |
draw_word_cloud |
Whether to draw the word clouds. |
term |
The full name or the description of the corresponding GO IDs. |
min_term |
Minimal number of functional terms in a cluster. All the clusters with size less than |
order_by_size |
Whether to reorder clusters by their sizes. The cluster that is merged from small clusters (size < |
exclude_words |
Words that are excluded in the word cloud. |
max_words |
Maximal number of words visualized in the word cloud. |
word_cloud_grob_param |
A list of graphic parameters passed to |
fontsize_range |
The range of the font size. The value should be a numeric vector with length two. The minimal font size is mapped to word frequency value of 1 and the maximal font size is mapped to the maximal word frequency. The font size interlopation is linear. |
bg_gp |
Graphics parameters for controlling word cloud annotation background. |
column_title |
Column title for the heatmap. |
ht_list |
A list of additional heatmaps added to the left of the similarity heatmap. |
use_raster |
Whether to write the heatmap as a raster image. |
run_draw |
Internally used. |
... |
Other arguments passed to |
A HeatmapList-class object.
mat = readRDS(system.file("extdata", "random_GO_BP_sim_mat.rds",
package = "simplifyEnrichment"))
cl = binary_cut(mat)
ht_clusters(mat, cl, word_cloud_grob_param = list(max_width = 80))
ht_clusters(mat, cl, word_cloud_grob_param = list(max_width = 80),
order_by_size = TRUE)