This repository contains code and resources for analyzing Liver Hepatocellular Carcinoma (LIHC) data. The goal of this project is to explore clinical, molecular, and genomic features of LIHC datasets, generating visualizations, statistics, and insights that may improve understanding of liver cancer.

Table of Contents

• Features
• Project Structure
• Requirements
• Installation
• Usage
• Contributing
• License
• Contact

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Features

• Data Preprocessing: Scripts to clean, filter, and normalize LIHC data.
• Statistical Analysis: Exploratory data analysis, hypothesis testing, and basic modeling.
• Visualization: Generation of plots and charts (e.g., survival curves, heatmaps).
• Reproducible Workflows: Notebook- or script-based pipelines to reproduce results.

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Project Structure

A typical layout for the repository might be:

LIHC-project/
├─ data/               # Raw and processed data files
├─ scripts/            # Standalone data processing scripts
├─ notebooks/          # Jupyter (or R) notebooks for exploratory analysis
├─ results/            # Outputs from analyses (figures, tables)
├─ figures/            # Visualizations generated by the scripts/notebooks
├─ README.md           # Project documentation (you are here!)
└─ LICENSE             # License information (if applicable)

Note: File organization may differ from what you see above. Adjust as needed if your repository has a different structure.

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Requirements

• Python >= 3.X (or R if the analysis uses R scripts)
• Common data science libraries (e.g., pandas, numpy, matplotlib, seaborn, scikit-learn)
  OR
  Common R packages (e.g., tidyverse, survival, ggplot2)
• [Optional] Additional libraries for statistical analysis or plotting.

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Installation

1. Clone the repository:

   git clone https://github.com/koehlek99/LIHC-project.git
   cd LIHC-project

2. Set up a virtual environment (recommended):

   python -m venv venv
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

   Or install packages as needed (if a requirements.txt is not yet provided).

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Usage

1. Prepare input data

   • Ensure required datasets are placed in the data/ directory, or specify their paths in your scripts or notebooks.

2. Run analysis

   • Check the scripts/ folder or relevant notebooks to see how to run analyses. For example:

     python scripts/preprocess_data.py
     python scripts/analyze_data.py

     Or open any Jupyter notebook in notebooks/:

     jupyter notebook notebooks/survival_analysis.ipynb

3. View results

   • Generated figures or tables will appear in the results/ or figures/ folders.

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Contributing

Contributions are welcome! If you would like to contribute:

1. Fork the repo and create a new branch for your feature or bugfix.
2. Commit your changes with clear messages.
3. Submit a pull request explaining what you changed and why.

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License

If this project is using a specific license (e.g., MIT, Apache 2.0), include the details here. If not specified, you can remove this section or add a note that it’s for internal or personal use only.

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Contact

For questions, feedback, or to discuss potential collaborations:

• Author: koehlek99, bibymaths, sankeg99
• Alternatively, open an issue on this repository.

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Feel free to modify any section to align with your specific needs and the exact contents of the repo.