Metadata-Version: 2.4
Name: proteomicscopylot
Version: 0.1.1
Summary: ProteomicsCoPYlot project
Home-page: https://github.com/JonasMarx3007/ProteomicsCoPYlot
Author: <Jonas Marx>
Author-email: <jonas.marx3007@gmx.de>
License: MIT
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=2.0
Requires-Dist: numpy>=1.23
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Requires-Dist: scikit-learn>=1.2
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Requires-Dist: statsmodels>=0.14
Requires-Dist: plotly>=5.15
Requires-Dist: gprofiler-official>=1.0.0
Requires-Dist: biopython>=1.81
Requires-Dist: matplotlib-venn>=0.11
Requires-Dist: seaborn>=0.12
Provides-Extra: app
Requires-Dist: streamlit>=1.30; extra == "app"
Provides-Extra: dev
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Dynamic: author
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---
title: Proteomics CoPYlot
sdk: docker
app_port: 7860
---

# Proteomics CoPYlot (Python Version)

Proteomics CoPYlot is a **Python-based tool for proteomics data analysis and visualization**, designed to simplify common workflows and provide interactive plots and light statistics for your experimental results. It can be run **locally** as a Python script, executable or accessed via the **web** at [proteomics-data.com](https://proteomics-data.com).

---

## Features

- Upload and analyze **Spectronaut, DIA-NN, or MaxQuant output files**  
- Generate **quality control (QC) plots**: coverage, missing values, intensity distribution, and more  
- **Interactive visualizations** with adjustable plot size, resolution, and hover-names
- Export **presentation-ready figures** directly from the interface  
- Simple **statistical summaries** for quick assessment of datasets  

---

## Installation

```bash
# Clone the repository
git clone https://github.com/JonasMarx3007/ProteomisCoPYlot.git

# Navigate to the project folder
cd ProteomisCoPYlot

# Install dependencies
pip install -r requirements.txt
```

--- 

## Example Workflow

1. Upload your proteomics dataset (TSV or compatible format)  
2. Explore proteins and phosphosites interactively in the **Streamlit interface**  
3. Generate **quality control plots** with customizable details  
4. Export figures for reports or presentations

---

## Dependencies

Python ≥ 3.8  

Required Python packages:

- streamlit  
- matplotlib  
- pandas  
- numpy  
- scikit-learn  
- statsmodels  
- plotly  
- gprofiler-official  
- biopython
- matplotlib-venn
- seaborn

Install via pip:

```bash
pip install streamlit matplotlib pandas numpy scikit-learn statsmodels plotly gprofiler-official biopython matplotlib-venn seaborn
```

---

## Usage
Run the locally using Streamlit:
```bash
streamlit run app.py
```
Open http://localhost:8501 in your browser to interact with the application. Or alternatively run the run_app.py.

---

## License
This project is licensed under the **MIT License** — see the LICENSE file for details.
