Enrichment-Pipeline

Automatic R pipeline for FGSEA and classic enrichment (GO, KEGG, Reactome, Hallmark)

FGSEA & Classic Enrichment Pipeline

This R script provides a comprehensive workflow to perform gene set enrichment analysis from RNA-seq or similar gene expression results (scRNA-seq results for an specific cluster).

It combines:

• FGSEA (fast gene set enrichment analysis) for Hallmark, GO_BP, and Reactome pathways.
• Classic enrichment analysis using GO (BP, MF, CC), KEGG, Reactome, and Hallmark gene sets.
• Automatic plotting: dotplots, treeplots, cnetplots, and enrichment plots for top pathways.
• Exporting results: Excel files with enrichment results and PNG plots.

Features

• Separate analysis of up- and down-regulated genes.
• Configurable thresholds (padj, log2FC, pvalue, qvalue).
• Robust handling of Excel sheets, missing data, and folder creation.
• Built-in logs for reproducibility and debugging.
• Highlighting system in plots:
  • flexible_terms: highlights any term that contains the provided substring (e.g., "mito" → highlights terms like mitochondrial matrix or mitochondrial membrane).
  • specific_terms: highlights only exact matches (e.g., "Transmembrane Transporter Complex").
  • specific_genes: highlights GO terms that include certain genes of interest within their gene list.
  • Customizable color and text size for all highlighted terms.

Requirements

• R >= 4.5.0
• Packages: readxl, ggplot2, ggrepel, clusterProfiler, org.Mm.eg.db, org.Hs.eg.db, openxlsx, ReactomePA, msigdbr, enrichplot, fgsea, data.table, ggtext, dplyr.

Data frame requirements

The input DEG (Differentially Expressed Genes) file or data frame must contain the following columns:

Column	Description	Accepted Names
SYMBOL	Gene name or gene symbol.	symbol, gene, genesymbol, genename
log2FoldChange	Log₂ fold change value for each gene.	log2foldchange, logfc, foldchange, logfold
padj	Adjusted p-value (or FDR). Used to select significant DEGs.	padj, adjpval, adj, pvaladj, pvalueadj, pvalueadjusted, pvaladjusted, padjval, padjustedval, padjvalue, padjustedvalue
pvalue	Raw p-value column	pval, pvalue, p, pv

Quick Installation

To clone the repository and access all scripts locally:

git clone https://github.com/AlbertoTh98/Enrichment-Pipeline.git
cd Enrichment-Pipeline

Example usage

excel_path <- "/path/to/your/data.xlsx"
deg_dataframe <- FindMarkers(seurat_obj, ident.1 = "Cluster3", ident.2 = NULL)

run_enrichment_pipeline(
   input_data = excel_path,                                   # or just the deg_dataframe
   sheet_name = "Sheet1",                                     # only needed if input_data is an Excel file
   padj_cutoff = 0.05,                                        # adjusted p-value cutoff for DEGs
   log2fc_cutoff = 0,                                         # log2 fold-change threshold
   pval_cutoff = 0.05,                                        # p-value cutoff for enrichment
   qval_cutoff = 0.1,                                         # q-value cutoff for enrichment
   output_dir = "/path/to/output_folder",                     # directory where results will be saved
   n_terms = 30,                                              # number of top terms to display in plots
   flexible_terms = c("mito", "resp"),                        # highlight any term containing these substrings "mito" and "resp" are examples for mitochondria or respiratory terms
   specific_terms = c("Transmembrane Transporter Complex"),   # highlight exact term(s)
   specific_genes = c("Mki67", "Arap2", "Prok2", "Syne2"),    # highlight terms containing these genes
   color_terms = "#0052DB",                                   # color used to highlight terms in plots, default value is darkred
   size_terms = 12                                            # text size for highlighted terms
)