ht_clusters {simplifyEnrichment} | R Documentation |
Visualize the similarity matrix and the clustering
ht_clusters(mat, cl, dend = NULL, col = c("white", "red"), draw_word_cloud = is_GO_id(rownames(mat)[1]) || !is.null(term), term = NULL, min_term = 5, order_by_size = FALSE, cluster_slices = FALSE, exclude_words = character(0), max_words = 10, word_cloud_grob_param = list(), fontsize_range = c(4, 16), column_title = NULL, ht_list = NULL, use_raster = 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 < |
cluster_slices |
Whether to cluster slices. |
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. |
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. |
... |
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)