RNA-seq Analysis Pipeline Using R

A modular RNA-seq analysis workflow implemented in R for differential gene expression and functional enrichment analysis.

Overview

This repository provides a streamlined RNA-seq analysis pipeline covering data preparation, differential expression testing, visualization and pathway enrichment. The workflow is based on standard Bioconductor tools and has been adapted from teaching material developed for the Computational Omics course at Imperial College London.

Features

• Data preparation and quality control
• Differential expression analysis using DESeq2
• Principal Component Analysis (PCA)
• Volcano plot generation
• Gene Set Enrichment Analysis (GSEA)
• Gene Ontology (GO) over-representation analysis
• Key gene box plot which focuses on genes of interest
• Modular script structure for easy customization

Workflow

Raw count matrix
        ↓
Data preparation
        ↓
DESeq2 differential expression analysis
        ↓
PCA visualization
        ↓
Volcano plot visualization
        ↓
GSEA pathway enrichment
        ↓
GO enrichment analysis
        ↓
Key gene heatmap analysis

Repository Structure

Script	Purpose
00_config.R	Configuration and parameter settings
01_prepare_data.R	Import and prepare count data
02_run_deseq2.R	Differential expression analysis
03_plot_pca.R	PCA visualization
04_plot_volcano.R	Volcano plot generation
05_run_gsea.R	Gene Set Enrichment Analysis
06_run_go_ora.R	Gene Ontology enrichment analysis
07_plot_key_gene_heatmap	Key gene analysis

Requirements

• R (≥ 4.0)
• DESeq2
• ggplot2
• clusterProfiler
• enrichplot
• fgsea
• dplyr

Author

Developed as part of computational biology and bioinformatics training in DoLS, Imperial College London, with modifications to improve flexibility and reproducibility of RNA-seq analysis workflows.