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.

Key Parameters

• showCategory (5): Number of enriched terms/categories to display in the plot.
• color ("p.adjust"): Variable used to color nodes or bars (e.g., "pvalue", "p.adjust", "qvalue").
• layout ("kk"): Layout algorithm for network plots like cnetplot or emapplot.
• foldChange (NULL): Numeric vector of fold changes to color gene nodes in cnetplot or heatplot.
• geneSetID: Numeric index or character ID of the gene set to plot in gseaplot2.

Best Practices

• Calculate Similarities: Calculate pairwise term similarities using pairwise_termsim() before running emapplot() or treeplot().
• Identify Hub Genes: Use cnetplot to identify hub genes that contribute to multiple enriched pathways.
• Avoid Overcrowding: Adjust the showCategory parameter to avoid overcrowded plots when dealing with many enriched terms.
• Customize with ggplot2: Leverage ggplot2 functions (e.g., theme(), labs()) to customize the output plots since all enrichplot figures are standard ggplot objects.

Common Pitfalls

• Missing Similarity Matrix: Running emapplot or treeplot throws an error about missing term similarity; fix this by running x <- pairwise_termsim(x) on your enrichment object first.
• Overlapping Labels: Node labels in network plots overlap and are unreadable; fix this by setting cex_label_category or using ggrepel options if supported.
• Mismatched Gene IDs: Gene names in cnetplot are displayed as cryptic Entrez IDs; fix this by using setReadable() from clusterProfiler to translate IDs to gene symbols before plotting.

Alternatives

• clusterProfiler: Contains basic built-in plotting functions (barplots, dotplots) but lacks advanced network visualizations.
• pathfindR: Offers active-subnetwork-oriented enrichment and visualization.
• enrichViewNet: Specifically designed for visualizing enrichment networks using interactive layouts.

Citations

• Yu, G. (2020). yulab-smu.top/biomedical-knowledge-mining-book/.

References

• Homepage: https://bioconductor.org/packages/enrichplot
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/enrichplot/inst/doc/enrichplot.html

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