Muscle_wasting

A bioinformatics pipeline for transcriptomic analysis of muscle wasting conditions. Integrates differential expression, transcription factor analysis, pathway enrichment, and network generation in a unified workflow.

🚀 Features

• run_DEA – Perform Differential Expression Analysis (DEA) and extract Differentially Dysregulated Sites (DDSs).
• run_DDs – Identify Differentially Expressed Genes (DEGs).
• run_TF – Predict and analyze transcription factors driving the DEGs.
• run_pathway – Conduct pathway enrichment on DEGs or TF targets.
• run_network_creation – Build regulatory networks based on TF–gene relationships.
• main_pipeline – Execute the full workflow in sequence.

📁 Repository Structure

Muscle_wasting/
├── common_tools/          # Shared scripts and utilities
├── database_analysis/     # Database-driven analyses
├── data/                  # Raw/processed input data
├── network/               # Network construction modules
├── paper_mining/          # PubMed paper mining scripts
├── cytoscape/             # Cytoscape-specific tools
├── Databases/             # External data resources
├── ncbi/                  # NCBI query modules
├── Tests/                 # Unit and integration tests
├── Constants.py           # Global constants and config
├── main.py                # CLI entrypoint; defines main_pipeline
├── requirements.txt       # Python dependencies
└── DE_genes.ods           # Example DEG output file

🛠️ Installation

Install required packages:

pip install -r requirements.txt

Main dependencies:

• pandas
• numpy
• scipy
• statsmodels (or DESeq2/edgeR via rpy2)
• networkx
• gseapy or enrichr
• py4cytoscape
• pytest

⚙️ Usage

As a library:

from main import run_DEA, run_DDs, run_TF, run_pathway, run_network_creation
dds = run_DEA(input_counts, sample_metadata)
degs = run_DDs(dds)
tfs = run_TF(degs)
pathways = run_pathway(degs)
network = run_network_creation(tfs, degs)

Via the command-line:

python main.py \
  --input counts.csv \
  --metadata meta.csv \
  --output_dir results/

This runs the full main_pipeline, sequentially performing:

1. DEA & DDS detection
2. DEG calling
3. TF analysis
4. Pathway enrichment
5. Network construction

Results are stored in results/ as CSV and graph files.

🧪 Testing

Run tests from the repo root:

pytest

📄 Output

• DDS list
• DEG list
• Predicted TFs and regulators
• Enriched pathway reports
• Network export formats (e.g. SIF, GraphML)

📚 Citation

If you publish results using this pipeline, please cite the original GuerreroVazquez/Muscle_wasting repository.

❓ Questions & Contributing

For bugs, feature requests, or contributions, please open an issue or pull request.

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Author

Karen Guerrero‑Vazquez
📧 GuerreroVazquez.Karen@gmail.com
GitHub: GuerreroVazquez