Overview

This project demonstrates an end-to-end RNA-seq differential expression (DE) workflow in Python using pydeseq2 (DESeq2-like negative binomial modeling). The notebook covers:

• loading and validating a raw count matrix
• constructing metadata from GEO annotations (Series Matrix)
• running DE with an explicit contrast
• QC/visualization (PCA, clustered heatmap)

• exporting results for downstream interpretation

• Links: GitHub
• Status: Complete

Why this project

This project asks which human genes change expression in SARS‑CoV‑2 positive vs negative samples (GSE152075) using a proper RNA‑seq DE workflow with pyDESeq2, producing interpretable QC and results visuals

Highlights

• Differential expression (pydeseq2)
  • Count-based modeling using a negative binomial GLM
  • Multiple testing correction (FDR / adjusted p-values)
  • Results table extraction via DeseqStats(…).results_df
• QC and visualization
  • PCA to inspect global sample structure (and detect technical effects)
  • Heatmap / clustermap of top DE genes (pattern-level interpretation)

Result

Figure
Figure 1: Volcano plot Tumor vs Normal
Figure 2: PCA plot Tumor vs Normal
Figure 3: Top 20 genes based on p-value

How to run

git clone https://github.com/Anwesha19-prog/RNA-seq-Differential-Expression
cd RNA-seq-Differential-Expression
pip install pandas numpy matplotlib seaborn scikit-learn pydeseq2
jupyter notebook Project-2_clean.ipynb