Enrichment visualization and annotation

enrichment-visualization-and-annotation

Summary

This skill organizes and visualizes pathway enrichment analysis results (enrichment scores, adjusted p-values, annotated plots) hierarchically by omics layer (genes, miRNA, proteins, lipids) and tool choice (clusterProfiler or biotranslator), producing publication-ready summary statistics and intermediate R objects for downstream integration.

When to use

After pathway enrichment analysis has been executed by clusterProfiler or biotranslator on differentially expressed features filtered by layer-specific p-value cutoffs (genes_genespval=1, mirna_genespval=1, proteins_genespval=0.5, lipids_genespval=0.5). Use this skill when you need to consolidate enrichment results across multiple omics layers into a coherent, hierarchically organized output structure for multi-omics integration or publication.

When NOT to use

• Enrichment analysis has not yet been executed — use the pathway enrichment routing skill first
• Results are from a single omics layer and multi-layer integration is not planned — simpler flat output structures may suffice
• Raw p-value tables have not been filtered by layer-specific cutoffs — apply filtering before visualization

Inputs

• enrichment scores table (from clusterProfiler or biotranslator execution)
• adjusted p-values table (from clusterProfiler or biotranslator execution)
• intermediate R objects from enrichment tool
• layer identifier (genes, miRNA, proteins, or lipids)
• tool selection identifier (clusterProfiler or biotranslator)

Outputs

• hierarchically organized enrichment results directory structure
• annotated enrichment plots (ggplot2/ComplexHeatmap)
• enrichment summary statistics tables
• intermediate R objects for downstream analysis
• layer-specific subdirectories with tool-prefixed naming

How to apply

Retrieve enrichment analysis outputs (enrichment scores and adjusted p-values tables) from the selected tool (clusterProfiler or biotranslator) and organize them hierarchically into layer-specific directories (e.g., /genes/clusterprofiler/, /mirna/biotranslator/, /proteins/, /lipids/). Generate annotated plots using ggplot2 and ComplexHeatmap to visualize enrichment statistics. Bundle all intermediate R objects, tabular results, and plots together in the output directory structure (user_defined_output_directory/{layer}/{tool_name}/), ensuring consistent naming and metadata across layers. This hierarchical organization enables cross-layer comparison and facilitates the subsequent multi-omics data integration step.

Related tools

• ggplot2 (Generate publication-ready annotated plots for enrichment statistics visualization)
• ComplexHeatmap (Create complex heatmap visualizations of enrichment results across layers and pathways)
• clusterProfiler (Source of enrichment scores and adjusted p-values for organization and visualization)
• biotranslator (Alternative source of enrichment scores and adjusted p-values for organization and visualization)
• R (Execution environment for organizing outputs and generating visualizations)

Evaluation signals

• Output directory structure matches expected hierarchy: /user_defined_output_directory/{layer}/{tool_name}/ with all four layers (genes, mirna, proteins, lipids) present
• All enrichment tables contain required columns: enrichment scores and adjusted p-values with no null/missing values in key statistics
• Annotated plots are generated for each layer-tool combination with labeled axes, legends, and statistical annotations visible
• Intermediate R objects are serialized and retrievable (e.g., .RData or .rds format) for downstream multi-omics integration
• File naming and directory structure is consistent across all layers and tool choices (e.g., consistent naming scheme for plots, tables)

Limitations

• Hierarchical organization requires user to pre-specify output_directory parameter; missing or inaccessible paths will cause failures
• Visualization tools (ggplot2, ComplexHeatmap) may produce large plots for datasets with hundreds of pathways; down-sampling or faceting strategies may be needed for readability
• The skill does not validate upstream enrichment results; garbage input (e.g., invalid p-value distributions, missing identifiers) will produce poorly annotated outputs
• Cross-layer visualization (e.g., joint heatmaps of genes and proteins) requires manual post-processing; this skill organizes layer-specific results only

Evidence

• [methods] Organize all enrichment results (tables, plots, and intermediate R objects) hierarchically into the output directory structure (e.g., /user_defined_output_directory/genes/biotranslator/ or /genes/clusterprofiler/) with annotated plots and summary statistics.: "Organize all enrichment results (tables, plots, and intermediate R objects) hierarchically into the output directory structure (e.g., /user_defined_output_directory/genes/biotranslator/ or"
• [methods] Data preprocessing | R packages: edger, limma, sva, ggplot2, ComplexHeatmap: "Data preprocessing | R packages: edger, limma, sva, ggplot2, ComplexHeatmap"
• [methods] Execute the selected tool (clusterProfiler or biotranslator) to compute pathway enrichment statistics including enrichment scores and adjusted p-values.: "Execute the selected tool (clusterProfiler or biotranslator) to compute pathway enrichment statistics including enrichment scores and adjusted p-values."
• [methods] The pipeline performs pathway enrichment analysis by allowing users to specify either clusterprofiler or biotranslator via the pea_genes parameter, with separate p-value cutoffs configurable for genes, miRNA, proteins, and lipids: "The pipeline performs pathway enrichment analysis by allowing users to specify either clusterprofiler or biotranslator via the pea_genes parameter, with separate p-value cutoffs configurable for"