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Differential gene expression analysis exercise for the course Analytical Methods in Cancer Genomics 2023. The goal is to perform analysis of RNA-Seq data.

R 100.00%
differential-expression-analysis rna-seq rna-seq-analysis

differential-gene-expression-analysis's Introduction

DESeq2 Analysis

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.

Dependencies

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.

Usage

  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.

References

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