simplifyGO | R Documentation |
Simplify Gene Ontology (GO) enrichment results
simplifyGO(
mat,
method = "binary_cut",
control = list(),
plot = TRUE,
verbose = TRUE,
column_title = qq("@{nrow(mat)} GO terms clustered by '@{method}'"),
ht_list = NULL,
...
)
simplifyEnrichment(...)
mat |
A GO similarity matrix. You can also provide a vector of GO IDs to this argument. |
method |
Method for clustering the matrix. See |
control |
A list of parameters for controlling the clustering method, passed to |
plot |
Whether to make the heatmap. |
verbose |
Whether to print messages. |
column_title |
Column title for the heatmap. |
ht_list |
A list of additional heatmaps added to the left of the similarity heatmap. |
... |
Arguments passed to |
This is basically a wrapper function that it first runs cluster_terms()
to cluster
GO terms and then runs ht_clusters()
to visualize the clustering.
The arguments in simplifyGO()
passed to ht_clusters()
are:
draw_word_cloud
: Whether to draw the word clouds.
min_term
: Minimal number of GO terms in a cluster. All the clusters
with size less than min_term
are all merged into one single cluster in the heatmap.
order_by_size
: Whether to reorder GO clusters by their sizes. The cluster
that is merged from small clusters (size < min_term
) is always put to the bottom of the heatmap.
stat
: What values of keywords are used to map to font sizes in the word clouds.
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 word_cloud_grob()
.
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
: Graphic parameters for controlling the background of word cloud annotations.
A data frame with two columns: GO IDs and cluster labels.
set.seed(123)
go_id = random_GO(500)
mat = GO_similarity(go_id)
df = simplifyGO(mat, word_cloud_grob_param = list(max_width = 80))
head(df)
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