Functional Enrichment Analysis using Overrepresentation Analysis (ORA)

🔍 Introduction

This task wraps g:Profiler to turn long gene lists into interpretable biology. Starting from your DEGs (or any gene list), it performs Over-Representation Analysis (ORA) against well-curated knowledge bases (GO: BP/MF/CC, KEGG, …), applies multiple-testing correction (g:SCS by default), and returns clean tables plus ready-to-use visualizations.

Use it when you want quick, reproducible functional insights without maintaining local annotation databases. The task also builds an interactive Terms × Genes grid so you can see which genes drive each enriched term at a glance.

In this demonstration, data were retrieved from this article "Airway Smooth Muscle–Specific Transcriptomic Signatures of Glucocorticoid Exposure"

🧰 Prerequisites

• Access to Constellab and a valid Digital Lab environment
• Installed bricks: gws_omix ≥ 0.11.12
• A CSV/TSV results file (e.g., from DESeq2 or edgeR) containing: an ID column (Ensembl, symbols, or mixed), optionally p-values / padj and log2FoldChange (used for filtering & bubble sizes)
• an ID column (Ensembl, symbols, or mixed),
• optionally p-values / padj and log2FoldChange (used for filtering & bubble sizes)

🧪 Workflow: Step-by-Step

1. Add the task Select OmiX – Functional enrichment analysis based on ORA in Constellab.
2. Provide input Attach your DE/Full results table (CSV/TSV).
3. Configure parameters

• organism_name – Scientific name (e.g., Homo sapiens).
• genes_colname – Which column contains gene IDs (Ensembl or symbols).
• padj_threshold – FDR cutoff to filter DEGs before ORA (e.g., 0.05).
• abs_log2fc – |log2FC| cutoff for UP/DOWN subsets (optional; 0 to disable).
• sources_list – Comma-separated sources to test (default: GO:BP,GO:MF,GO:CC,KEGG).
• topn_plot – Top-N terms for static barplots (default: 20).
• grid_genes_per_page – Page size for the interactive grid (default: 25).

Tip: If your table contains mixed identifiers (Ensembl + symbols), the task internally normalizes IDs via g:Profiler ,no manual preprocessing needed.

     4.   Run the task

📤 Outputs

You’ll get three output groups:

1) ORA tables

One CSV per gene set (ALL_DE, UP_DE, DOWN_DE).

2) Barplots 

For each gene set, a static Top-N barplot of most significant terms (default N=20).

3) Interactive Terms × Genes

A browsable grid where:

• Rows = enriched terms (grouped by source; long labels stay readable)
• Columns = genes (paginated if many)
• Bubble color = term p-value (more intense = more significant)
• Bubble size = |log2FC| of the gene (if available; else a default size)
• Tooltips show exact term, gene, and p-value; a colorbar explains the scale.