Bio Pathway Enrichment Visualization

Version Compatibility

Reference examples tested with: enrichplot 1.30+, clusterProfiler 4.18+, ggplot2 3.5+.

Before using code patterns, verify installed versions match. If versions differ:

• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

The single biggest hazard here is the cnetplot/emapplot/goplot API churn. enrichplot 1.25.5 (2024-10) moved these to the ggtangle backend and DROPPED several old arguments (cex_label_category, cex_label_gene, circular, colorEdge, group/group_category/group_legend). The examples target the post-churn API (1.30+), but installed bases span three argument generations - run ?cnetplot / ?emapplot and adapt rather than pinning one generation.

Enrichment Visualization

"Make a figure from my enrichment results" -> Render an enrichResult or gseaResult with enrichplot, choosing how the gene-set REDUNDANCY is handled - because a raw top-N plot is one biological theme drawn N times, not N findings.

• R: dotplot(ego, showCategory=20); redundancy as structure via emapplot(pairwise_termsim(ego))

Scope: turn an enrichResult/gseaResult/compareClusterResult into a figure, and decide whether to SHOW or DELETE redundancy. The ORA/GSEA statistics that produce the objects -> go-enrichment, gsea. The method-selection fork lives in the category README; this skill already explains the redundancy (DAG/nesting) it renders. simplify() existence (the GO-DAG dedup) -> go-enrichment. Generic ggplot2 grammar (scales, themes, faceting) -> data-visualization/ggplot2-fundamentals. Cytoscape UI mechanics -> data-visualization/network-visualization.

The Single Most Important Modern Insight -- An Enrichment Figure Is a Modeling Choice, Not a Rendering of a Table

The default dotplot(ego, showCategory=20) is almost never twenty findings. The GO DAG and nested pathway databases guarantee that a real signal surfaces as a CLUSTER of near-identical overlapping terms driven by the same handful of genes: if "mitotic cell cycle" is enriched, then "cell cycle process," "cell cycle," "cell division," and a dozen ancestors and siblings enrich too. Sorting by p-value floats that redundant cluster to the top, so the figure shows ONE theme twenty times and crowds out the second and third themes entirely. The reader infers twenty independent findings; the figure lies by omission.

So the load-bearing question is never "which plotting function" but three decisions:

1. How is the redundancy collapsed? SHOW it as structure (pairwise_termsim -> emapplot/treeplot, or EnrichmentMap) so the cluster size conveys support, or DELETE it (simplify() for a shorter GO list, REVIGO for a flat-list treemap). Plotting raw top-20 with no collapse step is the error the whole skill exists to prevent.
2. Is the direction kept? GSEA results are SIGNED (NES > 0 = activated, NES < 0 = suppressed). Any GSEA figure that maps magnitude to a bar height or |NES|, or colors by a one-sided p-value ramp, silently merges activation and suppression. enrichplot deliberately ships NO barplot method for gseaResult for exactly this reason - a bar from zero cannot carry a sign.
3. Does the caption admit truncation? showCategory=20 is a window, not a census. If 200 terms passed FDR it is a 10% sample chosen by whatever orderBy used. Report the total significant count and the similarity method=/min_edge= settings - two honest analysts get different emapplot modules from the same object.

The Object Model -- What Gets Plotted

Every enrichplot function dispatches on the S4 class of its input, and the SAME function name encodes different things by class:

• enrichResult (ORA: enrichGO/enrichKEGG/enricher) - columns ID, Description, GeneRatio, BgRatio, pvalue, p.adjust, qvalue, geneID, Count.
• gseaResult (GSEA: gseGO/gseKEGG/GSEA) - columns ID, Description, setSize, enrichmentScore, NES, pvalue, p.adjust, qvalue, rank, leading_edge, core_enrichment, plus the @geneList slot (the ranked named vector that drove the analysis).
• compareClusterResult (compareCluster) - stacked results across gene lists; the substrate for faceted dotplots.

Encoding definitions (verified): GeneRatio = k/n (k = query genes annotated to the term, n = query genes mapped to any term; stored as the string "k/n"). Count = k (the numerator alone). BgRatio = M/N (M = universe genes annotated to the term, N = universe genes mapped). Fold enrichment = (k/n)/(M/N) = GeneRatio/BgRatio. GeneRatio is NOT effect size: a giant term (M=800) can post a large GeneRatio with trivial enrichment, while a small term (M=5, k=3) shows a modest GeneRatio but huge fold enrichment. The p-value, not GeneRatio, is the test statistic. dotplot can put GeneRatio OR Count on x; size = Count, color = p.adjust by default.

Tool Taxonomy

Citations: dotplot/barplot/cnet/tree/upset/goplot/heatplot are enrichplot, paper-of-record clusterProfiler 4.0 (Wu 2021 The Innovation 2:100141). emapplot reimplements EnrichmentMap (Merico 2010 PLoS One 5:e13984; protocol Reimand 2019 Nat Protoc 14:482). simplify/Wang use GOSemSim (Yu 2010 Bioinformatics 26:976). REVIGO (Supek 2011 PLoS One 6:e21800). ridgeplot/gseaplot2 display the ES Subramanian 2005 PNAS 102:15545 defined.

Decision Tree by Intent

Dotplot -- the Three-Channel Summary

dotplot(object, x='geneRatio', color='p.adjust', showCategory=10, orderBy='x', label_format=30). The terms are ordered by orderBy='x' (the x variable), NOT by p-value, so by default the TOP dot is the highest GeneRatio, not the most significant. State the ordering or set it.

dotplot(ego, showCategory = 20)                                       # x = GeneRatio, size = Count, color = p.adjust
dotplot(ego, x = 'FoldEnrichment', showCategory = 20)                 # effect size = (k/n)/(M/N), not GeneRatio
dotplot(gse, x = 'NES', showCategory = 20, color = 'p.adjust')        # signed GSEA summary (dotplot dispatches on gseaResult)
dotplot(gse, showCategory = 20, split = '.sign') + facet_grid(~.sign) # split GSEA up vs down

For a compareClusterResult, dotplot.compareClusterResult defaults showCategory=5 per cluster and includeAll=TRUE (a term top-N in any cluster appears in every column).

Barplot -- ORA Only

barplot(height, x='Count', color='p.adjust', showCategory=8). There is NO barplot method for gseaResult (verified) - forcing a bar onto GSEA drops the NES sign. For signed GSEA use a NES dotplot, ridgeplot, or gseaplot2.

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

Show the Redundancy -- pairwise_termsim then emapplot/treeplot

Goal: Reveal that a block of near-identical enriched terms is one biological theme, by clustering terms on gene-set overlap and drawing the clusters.

Approach: Populate the term-similarity matrix first with pairwise_termsim (emapplot/treeplot READ x@termsim and do NOT compute it), then draw it as a force-directed map (emapplot, shows topology) or a deterministic Ward tree (treeplot, named clusters). The similarity method= and min_edge= are modeling choices that change the picture.

ego_ts <- pairwise_termsim(ego)                          # JC (Jaccard on gene overlap), the default; any gene-set type
emapplot(ego_ts, showCategory = 30)                      # nodes = terms, edges = overlap >= min_edge (0.2), clusters = redundant groups
treeplot(ego_ts, showCategory = 30, nCluster = 5)        # deterministic Ward clustering into 5 labeled groups

# GO terms, DAG-aware similarity (Wang sees parent/child closeness even with modest gene overlap)
ego_ts <- pairwise_termsim(ego, method = 'Wang', semData = GOSemSim::godata('org.Hs.eg.db',

pairwise_termsim method is exactly one of {Resnik, Lin, Rel, Jiang, Wang, JC}, default JC. Resnik/Lin/Rel/Jiang/Wang are GO-ONLY and need a GOSemSimDATA object; JC works for any gene-set type. Lower min_edge and everything connects to everything (the "if every node touches every node, the result IS redundant" diagnostic); raise it and only the strongest overlaps survive.

Gene-Concept Network (cnetplot)

Goal: Show which genes are shared across enriched terms - the redundancy seen from the gene side - and their direction.

Approach: Draw a bipartite term-to-gene network, mapping the gene-node color to fold change. Keep to 5-8 terms or it collapses into a hairball. The ggtangle-era arguments differ from older tutorials - introspect before pinning args.

cnetplot(ego, showCategory = 5)                          # ggtangle backend (enrichplot >= 1.25.5)
cnetplot(ego, showCategory = 5, foldChange = gene_list)  # gene color by fold change; node_label = 'all'|'category'|'item'|'none'
# OLDER installed versions used: cnetplot(ego, foldChange=fc, circular=TRUE, colorEdge=TRUE) -- those args were REMOVED; run ?cnetplot

GSEA Plots -- ridgeplot, gseaplot2

ridgeplot(gse, showCategory=30, fill='p.adjust', core_enrichment=TRUE, orderBy='NES') draws, per set, a density of the @geneList metric values of its LEADING-EDGE genes. Shifted right = up-ranked, left = down-ranked, bimodal = the set straddles both extremes (often too broad). ridgeplot needs the ggridges package (an enrichplot Suggests-only dependency) or it errors. gseaplot2(gse, geneSetID, subplots=1:3) stacks the running ES curve, the hit ticks, and the ranked-metric profile; geneSetID is required and accepts an index, a vector (1:3 to overlay), or an ID string.

ridgeplot(gse, showCategory = 20)                        # direction + shape; the honest GSEA overview
gseaplot2(gse, geneSetID = 1:3, pvalue_table = TRUE)     # overlay three sets' running scores

Specialized Views -- upsetplot, goplot, heatplot

upsetplot(ego, n = 10)                                   # gene-overlap combinations across terms (gseaResult gives per-set metric boxplots)
goplot(ego)                                              # GO-ONLY: the induced DAG subgraph; needs the ggarchery package (enrichplot Suggests)
heatplot(ego, foldChange = gene_list, showCategory = 15) # gene x term matrix, color by direction; a flattened cnetplot

All Outputs Are ggplot Objects

Every enrichplot function returns a ggplot object, so chain ggplot2 modifiers and save with ggsave. Generic grammar (themes, scales, faceting) lives in data-visualization/ggplot2-fundamentals.

p <- dotplot(ego, showCategory = 20) + scale_color_viridis_c() + ggtitle('GO BP enrichment')
ggsave('fig.pdf', p, width = 10, height = 8)

Per-Method Failure Modes

Raw top-20 redundancy artifact

Trigger: dotplot(ego, showCategory=20) straight from enrichGO on GO results. Mechanism: the GO DAG guarantees a real signal surfaces as a nested cluster of overlapping terms driven by the same genes. Symptom: twenty bars/dots that are "cell cycle," "cell cycle process," "mitotic cell cycle," "cell division" - one theme repeated. Fix: simplify() for a shorter list, or pairwise_termsim -> emapplot/treeplot to show the structure, or REVIGO/EnrichmentMap.

Missing pairwise_termsim

Trigger: emapplot(ego) or treeplot(ego) without the precursor. Mechanism: these read x@termsim, an empty slot until populated. Symptom: an error about a missing termsim slot, or an empty map. Fix: ego_ts <- pairwise_termsim(ego) first, every time.

Barplot on gseaResult / dropped NES sign

Trigger: coercing a gseaResult to a data frame and bar/dot-plotting |NES| or a p-value ramp. Mechanism: a bar from zero is unsigned; |NES| merges activated and suppressed pathways. Symptom: a figure that hides that half the pathways are suppressed. Fix: there is deliberately no barplot for gseaResult; use a diverging color-by-NES dotplot, ridgeplot, or gseaplot2.

GeneRatio read as effect size

Trigger: "term A has GeneRatio 0.6 so it is strongly over-represented." Mechanism: GeneRatio is k/n, not the fold enrichment (k/n)/(M/N). Symptom: a giant uninformative term ranked above a small specifically-enriched one. Fix: use x='FoldEnrichment' (or GeneRatio/BgRatio) when specificity is the point; report the p-value as the test statistic.

Default-ordering misread

Trigger: reading the top dot of a default dotplot as "most significant." Mechanism: orderBy='x' orders by the x variable (GeneRatio), not p.adjust. Symptom: a low-significance high-GeneRatio term presented as the headline. Fix: order/color by p.adjust explicitly, or state the ordering in the caption.

Over-trimmed showCategory

Trigger: showCategory=20 when 200 terms passed FDR. Mechanism: showCategory truncates to a top-N window by whatever orderBy used. Symptom: a 10% sample read as the complete result. Fix: report the total significant count and selection criterion in the caption; the figure is a window, not a census.

Pinned deprecated enrichplot args

Trigger: copying circular=TRUE, colorEdge=TRUE, cex_label_gene=, cex_label_category=, or group_category= from a pre-2024 tutorial. Mechanism: enrichplot 1.25.5+ moved cnet/emap/goplot to ggtangle and removed those arguments. Symptom: an unused-argument error or a silently ignored arg. Fix: ?cnetplot / ?emapplot and use the current arguments (color_item, size_category, node_label, node_label_size, min_edge).

Wang/IC similarity on non-GO results

Trigger: pairwise_termsim(kegg_result, method='Wang'). Mechanism: Resnik/Lin/Rel/Jiang/Wang require the GO DAG and a GOSemSimDATA object. Symptom: an error or a meaningless similarity for KEGG/Reactome/custom sets. Fix: use method='JC' (gene overlap) for any non-GO gene set.

Quantitative Thresholds

Common Errors

References

• Wu T, Hu E, Xu S, et al. 2021. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation 2:100141.
• Yu G, Li F, Qin Y, Bo X, Wu Y, Wang S. 2010. GOSemSim: an R package for measuring semantic similarity among GO terms and gene products. Bioinformatics 26:976-978.
• Supek F, Bosnjak M, Skunca N, Smuc T. 2011. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6:e21800.
• Merico D, Isserlin R, Stueker O, Emili A, Bader GD. 2010. Enrichment Map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One 5:e13984.
• Reimand J, Isserlin R, Voisin V, et al. 2019. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap. Nat Protoc 14:482-517.
• Subramanian A, Tamayo P, Mootha VK, et al. 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. PNAS 102:15545-15550.

Related Skills

• go-enrichment - Produces the enrichResult; owns simplify() the GO-DAG dedup
• gsea - Produces the gseaResult; owns the enrichment score and leading-edge concept
• kegg-pathways - KEGG enrichResult/gseaResult to plot (pathview pathway-diagram overlay lives there)
• reactome-pathways - Reactome enrichResult/gseaResult to plot
• wikipathways - WikiPathways enrichResult/gseaResult to plot
• data-visualization/ggplot2-fundamentals - Generic ggplot2 grammar for the returned objects
• workflows/expression-to-pathways - End-to-end DE-to-enrichment-to-figure pipeline