This repository contains an R script for performing a gene expression analysis using DESeq2, a popular Bioconductor package for differential gene expression analysis. DESeq2 is widely used in RNA-seq data analysis to identify differentially expressed genes between different conditions or groups.

To run the script, you need to have the following R packages installed:

• DESeq2
• pheatmap

Make sure you have these packages installed before running the script.

1. Clone the repository or download the R script directly.

2. Prepare your input files:

   • sample.counts: This file should contain the count matrix with gene expression values. The rows represent genes, and the columns represent samples.
   • sample.info: This file should contain sample metadata, including information about the conditions or groups.

3. Update the file paths in the script:

   • Modify the file paths in the read.table() function calls to point to your actual input files.

4. Run the script:

   • Execute the script in your preferred R environment (e.g., RStudio) or via the command line.
   • The script will perform the following steps:
     • Load the count matrix and sample information.
     • Create a DESeq2 object.
     • Perform differential expression analysis.
     • Generate various plots, including an MA-plot, a plot of normalized counts, a PCA plot, and a heatmap.
     • Export the significant results to a CSV file.

5. The resulting plots will be saved as PNG files in the same directory as the script:

   • ma_plot.png: MA-plot showing the differential expression results.
   • plot_counts.png: Normalized counts plot for the GJB2 gene.
   • pca_plot.png: PCA plot of the samples.
   • heatmap.png: Heatmap visualization of differential gene expression results.

• DESeq2 Bioconductor package: Official documentation and resources for DESeq2.