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