gm_enrichcores: gm_enrichcores: GSEAmining core enrichment genes

In oriolarques/GSEAmining: Make Biological Sense of Gene Set Enrichment Analysis Outputs

gm_enrichcores: GSEAmining core enrichment genes

Description

Takes the output of gm_clust, which is an hclust class object, and plots the top n genes in core enrichment (leading edge analysis). Two options are available, either separate barplots by clusters or all together in one plot.

Usage

gm_enrichcores(
  df,
  hc,
  clust = TRUE,
  col_pos = "red",
  col_neg = "blue",
  top = 3
)

Arguments

df	Data frame that contains at least three columns: an ID column for the gene set names, a NES column with the normalized enrichment score and a core_enrichment column containing the genes in the leading edge of each gene set separated by '/'.
hc	The output of gm_clust, which is an hclust class object.
clust	A logical value indicating if wordclouds should be separated by clusters or not. Default value is TRUE.
col_pos	Color to represent positively enriched gene sets. Default is red.
col_neg	Color to represent negatively enriched gene sets. Default is blue.
top	An integer to choose the top most enriched genes to plot per cluster. The default parameter are the top 3.

Value

Returns a ggplot object.

Examples

data(genesets_sel)
gs.cl <- gm_clust(genesets_sel)
gm_enrichcores(genesets_sel, gs.cl)

oriolarques/GSEAmining documentation built on June 27, 2023, 11:31 p.m.