Exploratory Analysis of Human Transcriptomics Data

SARS-CoV-2 Infection Response Study

A complete end-to-end bioinformatics pipeline for analyzing differential gene expression, functional enrichment, and gene interaction networks in SARS-CoV-2 infected cells.

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📋 Table of Contents

• Overview
• Key Findings
• Features
• Installation
• Usage
• Project Structure
• Results
• Citation
• License

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🔬 Overview

This project presents a comprehensive exploratory analysis of human transcriptomic responses to SARS-CoV-2 infection using publicly available RNA-seq data (GSE147507). The pipeline integrates:

• Differential Expression Analysis (DESeq2-style normalization, statistical testing)
• Functional Annotation (Gene Ontology, KEGG pathway enrichment)
• Network Analysis (Protein-protein interactions, hub gene identification)
• LLM-Powered Interpretation (Plain-language biological summaries)

Dataset: GSE147507 from NCBI GEO
Samples: 20 (9 Mock controls, 8 SARS-CoV-2 infected, 3 drug-treated)
Platform: RNA-seq (Illumina NextSeq 500)
Cell Types: A549-ACE2, Calu-3 (human lung epithelial cells)

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🎯 Key Findings

Differential Expression

• 365 significantly altered genes (|log2FC| ≥ 1.5, FDR < 0.05)
• 331 upregulated (antiviral & inflammatory response)
• 34 downregulated (metabolic suppression)

Top upregulated genes:

• IFNB1 (6.47 log2FC) - Type I Interferon.
• TNF (5.56 log2FC) - Pro-inflammatory cytokine.
• IL6 (4.46 log2FC) - Cytokine storm mediator.
• CXCL2/3 (~5.2 log2FC) - Neutrophil chemotaxis.

Functional Enrichment

• 205 enriched biological processes (defense response to virus, transcriptional regulation)
• 67 enriched KEGG pathways (TNF signaling, NF-κB, interferon response)

Network Analysis

• 80 hub genes in highly connected network (density: 0.597)
• Top hubs: IRF1, FOSB, IER3, CXCL2, NFKBIZ (master regulators)
• 1,886 gene-gene interactions (co-expression network)

Therapeutic Targets Identified

1. IRF1 - Interferon regulatory factor (central hub)
2. NFKBIZ - NF-κB pathway regulator
3. TNF pathway - Anti-cytokine therapies (infliximab, adalimumab)
4. IL-6 - Tocilizumab (already FDA-approved for COVID-19)

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✨ Features

• ✅ Reproducible pipeline (phase-gate workflow)
• ✅ Publication-quality figures (12 high-resolution plots)
• ✅ Statistical rigor (FDR correction, multiple testing)
• ✅ Multi-tier LLM integration (Gemini, Groq, local fallback)
• ✅ Evidence-grounded interpretations (no hallucinations)
• ✅ Educational summaries (plain-language explanations)
• ✅ Version controlled (Git with descriptive commits)

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🛠️ Installation

Prerequisites

• OS: Windows 10/11 (optimized for PowerShell)
• Python: 3.13.2
• RAM: 8GB+ recommended
• Storage: 2GB for data and results

Setup

# Clone repository
git clone https://github.com/YOUR_USERNAME/human-transcriptomics-analysis.git
cd human-transcriptomics-analysis

# Create virtual environment
python -m venv transcriptomics_env
.\transcriptomics_env\Scripts\Activate.ps1

# Install dependencies
pip install -r requirements.txt --break-system-packages

# Configure API keys (optional for LLM interpretation)
# Create .env file:
GEMINI_API_KEY=your_key_here
GROQ_API_KEY=your_key_here

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🚀 Usage

Quick Start

# Activate environment
.\transcriptomics_env\Scripts\Activate.ps1

# Run complete pipeline (sequential execution)
python scripts/01_inspect_data.py
python scripts/02_preprocess_normalize.py
python scripts/03_differential_expression.py
python scripts/04_functional_annotation.py
python scripts/05_network_analysis.py
python scripts/06_llm_interpretation.py

Alternative: Jupyter Notebook

# Launch Jupyter
jupyter notebook

# Open: notebooks/Complete_Analysis_Pipeline.ipynb

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📁 Project Structure

Transcriptomics_Project/
├── data/
│   ├── raw/                          # Original count matrix
│   │   └── covid19_raw_counts.tsv
│   ├── processed/                    # Normalized, filtered data
│   │   ├── counts_filtered_raw.csv
│   │   ├── counts_normalized.csv
│   │   └── counts_log2_transformed.csv
│   └── metadata/
│       ├── covid19_sample_metadata.txt
│       └── metadata_covid_vs_mock.csv
├── scripts/
│   ├── 01_inspect_data.py           # QC & data validation
│   ├── 02_preprocess_normalize.py   # DESeq2 normalization
│   ├── 03_differential_expression.py # DEG analysis
│   ├── 04_functional_annotation.py  # GO/KEGG enrichment
│   ├── 05_network_analysis.py       # PPI networks
│   └── 06_llm_interpretation.py     # LLM summaries
├── results/
│   ├── figures/                      # 12 publication plots
│   │   ├── 01_library_sizes.png
│   │   ├── 06_volcano_plot.png
│   │   └── 08_network_visualization.png
│   ├── tables/                       # CSV result files
│   │   ├── deg_significant_only.csv
│   │   ├── go_enrichment_*.csv
│   │   └── network_hub_genes.csv
│   ├── FINAL_REPORT.md              # Executive summary
│   ├── LLM_BIOLOGICAL_INTERPRETATION.md
│   └── EDUCATIONAL_SUMMARY.md
├── notebooks/
│   └── Complete_Analysis_Pipeline.ipynb
├── requirements.txt
├── .gitignore
├── .env                              # API keys (not committed)
└── README.md

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📊 Results

Key Visualizations

Volcano Plot (Differential Expression)

Network Visualization (Hub Genes)

PCA Analysis (Sample Clustering)

Output Files

File	Description	Records
deg_full_results.csv	All tested genes	13,803
deg_significant_only.csv	Significant DEGs	365
network_hub_genes.csv	Hub genes with centrality	80
go_enrichment_*.csv	Enriched GO terms/pathways	295

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📖 Documentation

• FINAL_REPORT.md - Executive summary with key findings
• METHODS_DOCUMENTATION.md - Detailed computational methods
• LLM_BIOLOGICAL_INTERPRETATION.md - Plain-language analysis
• EDUCATIONAL_SUMMARY.md - Student-friendly guide

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🧬 Biological Interpretation

SARS-CoV-2 infection triggers a coordinated transcriptional program:

1. Type I/III Interferon Response → Antiviral defense (IFNB1, IFNL1-3)
2. Pro-inflammatory Cytokines → Immune recruitment (TNF, IL6, IL1A)
3. Chemokine Secretion → Neutrophil attraction (CXCL2, CCL20)
4. Transcriptional Activation → NF-κB/IRF1 pathways
5. Metabolic Reprogramming → Resource allocation to immunity

Clinical Relevance:

• Cytokine storm pathways identified (TNF, IL-6)
• Therapeutic targets validated (tocilizumab, JAK inhibitors)
• Biomarker candidates for disease severity

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🔬 Methods Summary

Step	Method	Tool/Library
Quality Control	Library size filtering, gene filtering	pandas, matplotlib
Normalization	DESeq2 median-of-ratios	scipy, numpy
DEG Analysis	Welch's t-test + FDR correction	scipy.stats, statsmodels
Enrichment	Hypergeometric test	Enrichr API
Network	Gene co-expression (Pearson r ≥ 0.7)	networkx
Interpretation	LLM-powered summarization	Google Gemini / Groq

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🎓 Citation

If you use this pipeline or find these results useful, please cite:

@software{transcriptomics_pipeline_2026,
  author = {Your Name},
  title = {Exploratory Analysis of Human Transcriptomics Data: SARS-CoV-2 Response},
  year = {2026},
  url = {https://github.com/YOUR_USERNAME/human-transcriptomics-analysis}
}

Original Dataset:

• Blanco-Melo D, et al. (2020). Imbalanced Host Response to SARS-CoV-2 Drives Development of COVID-19. Cell. GSE147507.

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📜 License

This project is licensed under the MIT License - see LICENSE file for details.

Note: The GSE147507 dataset is publicly available from NCBI GEO under their terms of use.

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🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m "Add AmazingFeature")
4. Push to branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📧 Contact

Project Maintainer: P Sumanth

Email: sumanthp141005@gmail.com

GitHub: @Sumanth1410-git

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🙏 Acknowledgments

• NCBI GEO for providing public transcriptomics data
• Enrichr API (Ma'ayan Lab) for functional enrichment
• Google Gemini & Groq for LLM interpretation
• Open-source Python bioinformatics community

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⭐ If you found this project useful, please consider giving it a star!

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Last Updated: February 24, 2026
Status: ✅ Complete & Production-Ready