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.