Enrichment Visualization

Scope

This skill covers enrichplot package functions designed for clusterProfiler results:

• dotplot(), barplot() - Summary views
• cnetplot(), emapplot(), treeplot() - Network/hierarchical views
• gseaplot2(), ridgeplot() - GSEA-specific
• goplot(), heatplot(), upsetplot() - Specialized views

For custom ggplot2 enrichment dotplots (manual implementation), see data-visualization/specialized-omics-plots.

Setup

library(clusterProfiler)
library(enrichplot)
library(ggplot2)

# Assume ego (enrichGO result), kk (enrichKEGG result), or gse (GSEA result) exists

Dot Plot

Most common visualization - shows gene ratio, count, and significance.

dotplot(ego, showCategory = 20)

# Customize
dotplot(ego, showCategory = 15, font.size = 10, title = 'GO Enrichment') +
    scale_color_gradient(low = 'red', high = 'blue')

# Save
pdf('go_dotplot.pdf', width = 10, height = 8)
dotplot(ego, showCategory = 20)
dev.off()

Bar Plot

Shows enrichment count or gene ratio.

barplot(ego, showCategory = 20)

# Customize
barplot(ego, showCategory = 15, x = 'GeneRatio', color = 'p.adjust')

Gene-Concept Network (cnetplot)

Shows relationships between genes and enriched terms.

# Basic cnetplot
cnetplot(ego)

# With fold change colors
cnetplot(ego, foldChange = gene_list)

# Circular layout
cnetplot(ego, circular = TRUE, colorEdge = TRUE)

# Customize node size
cnetplot(ego, node_label = 'gene', cex_label_gene = 0.8)

Enrichment Map (emapplot)

Shows term-term relationships based on shared genes.

# Requires pairwise_termsim first
ego_pt <- pairwise_termsim(ego)
emapplot(ego_pt)

# Customize
emapplot(ego_pt, showCategory = 30, cex_label_category = 0.6)

# Cluster by similarity
emapplot(ego_pt, group_category = TRUE, group_legend = TRUE)

Tree Plot

Hierarchical clustering of enriched terms.

ego_pt <- pairwise_termsim(ego)
treeplot(ego_pt)

# Show more categories
treeplot(ego_pt, showCategory = 30)

Upset Plot

Show overlapping genes between terms.

upsetplot(ego)

# Limit to specific number of terms
upsetplot(ego, n = 10)

GSEA-Specific Plots

Running Score Plot (gseaplot2)

# Single gene set
gseaplot2(gse, geneSetID = 1, title = gse$Description[1])

# Multiple gene sets
gseaplot2(gse, geneSetID = 1:3)

# With subplots
gseaplot2(gse, geneSetID = 1, subplots = 1:3)

# By term ID
gseaplot2(gse, geneSetID = 'GO:0006955')

Ridge Plot

Distribution of fold changes in gene sets.

ridgeplot(gse)

# Top n gene sets
ridgeplot(gse, showCategory = 15)

# Order by NES
ridgeplot(gse, showCategory = 20) + theme(axis.text.y = element_text(size = 8))

GO-Specific Plot (goplot)

DAG structure of GO terms.

# Only for GO enrichment results
goplot(ego)

# Specific ontology
goplot(ego_bp)  # where ego_bp is enrichGO with ont='BP'

Heatplot

Gene-concept heatmap.

heatplot(ego, foldChange = gene_list)

# Customize
heatplot(ego, showCategory = 15, foldChange = gene_list)

Compare Multiple Analyses

# Compare clusters (from compareCluster)
dotplot(ck, showCategory = 10)

# Facet by cluster
dotplot(ck) + facet_grid(~Cluster)

Customize ggplot2 Elements

All enrichplot functions return ggplot2 objects.

p <- dotplot(ego, showCategory = 20)

# Add title
p + ggtitle('GO Biological Process Enrichment')

# Change theme
p + theme_minimal()

# Adjust text
p + theme(axis.text.y = element_text(size = 10))

# Change colors
p + scale_color_viridis_c()

Save Plots

# PDF (vector, publication quality)
pdf('enrichment_plots.pdf', width = 10, height = 8)
dotplot(ego, showCategory = 20)
dev.off()

# PNG (raster)
png('dotplot.png', width = 800, height = 600, res = 100)
dotplot(ego, showCategory = 20)
dev.off()

# Using ggsave
p <- dotplot(ego)
ggsave('dotplot.pdf', p, width = 10, height = 8)

Visualization Summary

Function	Best For	Input Type
dotplot	Overview of enrichment	ORA, GSEA
barplot	Simple counts/ratios	ORA
cnetplot	Gene-term relationships	ORA
emapplot	Term clustering	ORA
treeplot	Hierarchical grouping	ORA
upsetplot	Term overlap	ORA
gseaplot2	Running enrichment score	GSEA
ridgeplot	Fold change distribution	GSEA
goplot	GO DAG structure	GO only
heatplot	Gene-concept matrix	ORA

Related Skills

• go-enrichment - Generate GO enrichment results
• kegg-pathways - Generate KEGG enrichment results
• gsea - Generate GSEA results