🧬 Differential Gene Expression Analysis using DESeq2

This repository provides a complete R pipeline for performing Differential Gene Expression Analysis (DGEA) using the DESeq2 package. It supports raw non-decimal count data from RNA-seq (e.g., from HTSeq, featureCounts, etc.) and compares control vs disease conditions.

🚀 Features Input: CSV file with raw integer counts (genes as rows, samples as columns) Automatically installs and loads required packages

Generates: ✅ MA Plot ✅ Volcano Plot ✅ PCA Plot ✅ Heatmap of top differentially expressed genes

Outputs: differential_expression_results.csv with log2FC, p-values, padj, etc.

📂 How to Use Step 1: Modify These Two Things in the Script Path to your raw count file (in CSV format): count_file <- "C:/path/to/your_file.csv" Sample conditions (e.g., "control" or "treatment"), in the order your columns appear:

condition_groups <- c("control", "control", "treatment", "treatment") ⚠ Make sure your count data only contains non-decimal integer values, and column order matches the condition labels!

📦 Dependencies The script uses the following R packages: DESeq2 ggplot2 pheatmap RColorBrewer EnhancedVolcano These will be automatically installed if not present.

📊 Sample Output The script will create a results/ folder containing: MA_plot.png volcano_plot.png heatmap.png PCA_plot.png differential_expression_results.csv

📎 Sample outputs are attached to this repo for reference.

💡 Notes Input must be raw (unnormalized) integer counts — not TPM, FPKM, or log-transformed data. Genes with low counts are filtered before DE analysis. If your data is merged from multiple files, make sure all sample columns are numeric.