simplifyGO: Simplify Gene Ontology (GO) enrichment results

In simplifyEnrichment: Simplify Functional Enrichment Results

Description

Simplify Gene Ontology (GO) enrichment results

Usage

simplifyGO(mat, method = "binary_cut", control = list(),
    plot = TRUE, term = NULL, verbose = TRUE,
    column_title = qq("@{nrow(mat)} GO terms clustered by '@{method}'"),
    ht_list = NULL, ...)

Arguments

mat	A GO similarity matrix.
method	Method for clustering the matrix. See cluster_terms.
control	A list of parameters for controlling the clustering method, passed to cluster_terms.
plot	Whether to make the heatmap.
term	The full name or the description of the corresponding GO IDs. The values are automatically extracted if it is not provided.
column_title	Column title for the heatmap.
verbose	Whether to print messages.
ht_list	A list of additional heatmaps added to the left of the similarity heatmap.
...	Arguments passed to ht_clusters.

Details

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.

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.

Value

A data frame with three columns: GO IDs, GO term names and cluster labels.

Examples

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)

simplifyEnrichment documentation built on Nov. 8, 2020, 5:07 p.m.