🧬 Community Web

This is a Streamlit-based web interface for the Community R package, which enables interactive exploration of cell-to-cell communication differences using single-cell RNA sequencing (scRNA-seq) data.

🧠 About

Community detects and compares ligand-receptor interactions across case and control samples. This web frontend enhances accessibility through visual dashboards, data upload interfaces, dynamic tables, and visualizations including:

• Volcano plots
• Heatmaps
• Network plots
• Forest plots

All analysis runs locally, integrating Python and R.

🚀 Features

• 📂 Upload or use demo datasets for analysis.
• 🔍 Filter and visualize interactions using thresholds and statistical tests.
• 🖼️ Generate dynamic visualizations (plots, heatmaps, networks).
• 📊 Explore differential interactions and component-wise effects.
• 📁 Customize ligand-receptor database interactively.
• 🎨 Modify visualization parameters and styles.

🛠️ Tech Stack

• Frontend: Streamlit
• Backend: Python & R (via subprocess)
• Visualization: Plotly, Streamlit-AgGrid
• Analysis Engine: R package community

📦 Requirements

Install dependencies from requirements.txt:

pip install -r requirements.txt

You also need R installed with the following R packages:

• community
• data.table
• tidyverse
• gridExtra

🧩 Deploy Your Own Data

You can use your own single-cell RNA-seq data with the app by following these steps:

1. Prepare Your Data

Place your custom files in the input_data/ directory. The expected input files include:

• toy_counts.csv – Gene expression count matrix (genes × cells)
• toy_cell_annot.csv – Cell-level annotations (with cell type or cluster info)

Make sure your data follows the structure and formatting of the demo datasets provided in the same folder.

2. Customize Ligand-Receptor Database (Optional)

Optionally, you can edit or replace the file:

• input_data/ready_database.txt – The ligand-receptor interaction database used by the app.

This allows you to use a custom set of interactions relevant to your study.

3. Run the App

Once your files are ready, launch the app:

streamlit run app.py

Ensure R is properly installed and available in your system path, as it is called from Python using subprocess.

📁 Directory Structure

├── app.py                  # Main Streamlit app
├── backend.R               # Main R backend logic
├── heatmap.R               # Heatmap generation script
├── visualization.R         # Network/volcano plots
├── visualizations/         # Data and helper scripts for visualizations
├── plots/                  # Generated plot images
├── each_component_values/  # Interaction-level CSV data
├── input_data/             # Toy demo datasets (loaded dynamically)
└── requirements.txt

📄 License

This project is intended for research purposes. Refer to the source R package community for licensing.

🙌 Acknowledgements

• Developed on top of the community R package.
• Web interface built by Cem Güleç with the guidance of Muhammet A. Celik and Dr. Maria Solovey.