SurvSig

A web-based tool for complex gene signature analysis in neuroendocrine lung tumors and TCGA

📞 Access the application here: www.survsig.hcemm.eu
💡 No installation required – just open the website and start analyzing!
🔬 About our research group: www.hcemm.eu

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🚀 About SurvSig

SurvSig is an interactive web application designed for clinicians and researchers working with neuroendocrine lung tumors (SCLC, LCNEC, carcinoid tumors).
It enables users to analyze complex gene signatures and explore their relationship with patient outcomes through intuitive visualizations.

🛡️ Why use SurvSig?

✅ Designed for clinical research – No coding or bioinformatics expertise required.
✅ Supports multiple real-world datasets – Including SCLC, LCNEC, carcinoid tumors, and TCGA.
✅ User-friendly, web-based interface – Simply open the website and start analyzing.
✅ Gene signature-based analysis – Upload your own gene list or use predefined signatures.
✅ Advanced visualization tools – Explore survival plots, heatmaps, and UMAP clustering.

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📊 Key Features

✔ Multi-cohort analysis – Compare multiple patient datasets in one platform.
✔ Support for major datasets:

• 🦰 Small Cell Lung Cancer (SCLC): George-SCLC, Liu-SCLC, Lissa-SCLC, Jiang-SCLC
• 🧬 Large-Cell Neuroendocrine Carcinoma (LCNEC): George-LCNEC
• 🩺 Carcinoid Tumors: Alcala-Carcinoid, Fernandez-Carcinoid
• 🌐 Mixed Cohort Data: Rousseaux Lung Tumors, TCGA
  ✔ Machine learning-powered insights – Identify molecular subtypes with advanced clustering methods.
  ✔ Custom gene list support – Use predefined or user-defined gene signatures for analysis.

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🛠️ How to Use SurvSig (Online)

1️⃣ Visit the website: www.survsig.hcemm.eu
2️⃣ Select a dataset and analysis type.
3️⃣ Upload a gene list or choose from predefined signatures.
4️⃣ Explore results: View survival analysis, enrichment scores, heatmaps, and clustering outputs.

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🖥 How to Install Locally (Optional)

If you want to run SurvSig locally and upload custom datasets, follow these steps:

Prerequisites

• Python >= 3.8
• Git installed (git --version to check)
• Pipenv (recommended) or pip

Installation Steps

1️⃣ Clone the repository

git clone https://github.com/HCEMM/SurvSig.git
cd SurvSig

2️⃣ Set up the environment
Using pipenv (recommended):

pip install pipenv
pipenv install
pipenv shell

Or using pip:

pip install -r requirements.txt

3️⃣ Run the application

streamlit run main.py

4️⃣ Open the application

• After running the command, the app will start at:
  http://localhost:8501/

How to Add Custom Data?

• Place your gene expression matrix and clinical data into the /source_data folder.
• Integrate your dataset into the code

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📚 Citation

If you use SurvSig in your research, please cite:
📚 Nemes et al., 2025 (manuscript in preparation)

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📝 License

SurvSig is developed at HCEMM and is available under the GLP v3.