🧬 RNA-Seq Differential Expression Analysis Workflow

📌 Project Overview

This repository implements an end-to-end RNA-Seq differential expression analysis workflow based on:

Love MI, Anders S, Kim V, and Huber W (2015).
RNA-Seq workflow: gene-level exploratory analysis and differential expression.
F1000Research 4:1070.
https://doi.org/10.12688/f1000research.7035.1

The workflow demonstrates gene-level exploratory data analysis and differential expression testing using Bioconductor and DESeq2.

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🎯 Objective

• Perform RNA-Seq count-based differential expression analysis
• Explore sample relationships using PCA and clustering
• Identify significantly differentially expressed genes
• Visualize results using heatmaps and volcano plots
• Implement reproducible bioinformatics workflow in Google Colab

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🧪 Dataset

This project uses the airway dataset from Bioconductor, which contains RNA-Seq data from airway smooth muscle cells treated with dexamethasone.

Experimental Design:

• 4 human airway smooth muscle cell lines
• Each cell line: treated (trt) vs untreated (untrt)
• Paired experimental design: ~ cell + dex

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🛠 Tools & Packages Used

• R
• Bioconductor
• DESeq2
• airway (example dataset)
• pheatmap
• ggplot2
• RColorBrewer

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🔬 Workflow Steps

1️⃣ Load RNA-Seq Count Data

• Import SummarizedExperiment object
• Inspect sample metadata

2️⃣ Pre-filtering

• Remove genes with very low counts

3️⃣ Differential Expression Analysis

• Estimate size factors
• Estimate dispersion
• Fit generalized linear model
• Perform Wald test

4️⃣ Variance Stabilization

• Apply rlog transformation

5️⃣ Exploratory Data Analysis

• PCA plot
• Sample-to-sample distance heatmap

6️⃣ Visualization

• Volcano plot
• Heatmap clustering

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📊 Results

• PCA shows clustering by treatment and cell line
• Significantly differentially expressed genes identified
• Upregulated and downregulated genes detected at FDR < 0.1

Output Files:

• PCA_plot.png
• heatmap.png
• volcano_plot.png
• DESeq2_results.csv

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🚀 How to Run

This workflow was executed using Google Colab with R installed.

Steps:

1. Install R and Bioconductor packages
2. Load airway dataset
3. Run DESeq2 pipeline
4. Generate plots and export results

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📚 Reference

Love MI, Anders S, Kim V, and Huber W (2015).
RNA-Seq workflow: gene-level exploratory analysis and differential expression.
F1000Research 4:1070.
https://doi.org/10.12688/f1000research.7035.1

This article is distributed under the Creative Commons Attribution License (CC BY).

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👨‍💻 Author

Anurag Yadav
M.Tech Bioinformatics
Machine Learning & Bioinformatics Enthusiast

GitHub: https://github.com/ResidueRunner LinkedIn: www.linkedin.com/in/anurag-yadav-631533159

📂 Repository Structure