enrichplot

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page, reproduce in any R environment.

• Version: 1.32.0 · Bioconductor: 3.23 · R: ≥ 4.6
• Imports: aplot, DOSE, dplyr, enrichit, ggfun, ggnewscale, ggplot2, ggrepel, ggtangle, ggtree, GOSemSim, igraph, purrr, RColorBrewer, reshape2, rlang, scatterpie, tidydr, yulab.utils
• Install: BiocManager::install("enrichplot")

When to Use

• Enrichment Visualization: Visualizing functional enrichment results (Over-Representation Analysis [ORA] or Gene Set Enrichment Analysis [GSEA]) generated by clusterProfiler, DOSE, meshes, or ReactomePA.
• Network Plots: Creating advanced network plots (e.g., cnetplot to show gene-concept networks, emapplot for enrichment map plots).
• GSEA Running Scores: Generating publication-ready GSEA running score plots (gseaplot2) showing the distribution of enrichment scores across ranked gene lists.
• Functional Clustering: Visualizing functional similarities and clustering of enriched terms using tree plots (treeplot) or heatplots (heatplot).

When NOT to Use

• For performing the actual enrichment calculations, use clusterProfiler or fgsea instead because enrichplot is strictly a visualization package.
• For generic, non-biological network visualizations, use ggraph or igraph directly to avoid enrichment-specific object constraints.

Data Requirements

• Enrichment Result: An enrichment result object (typically of class enrichResult or gseaResult from clusterProfiler or related packages).
• Fold Changes: For gene-concept networks, a named vector of fold changes (numeric) is highly recommended to color gene nodes.