# Enrichment Visualization And Annotation

> Use when 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).

- Skill: `holobiomicslab/enrichment-visualization-and-annotation` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/enrichment-visualization-and-annotation`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/enrichment-visualization-and-annotation/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/enrichment-visualization-and-annotation

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# 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"