ria-gui 1.7.9

RIA: Ratio Imaging Analyzer for biological quantification

Ratio Imaging Analyzer (RIA / 莉丫)

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Meet RIA (or as we affectionately call her, "Li Ya / 莉丫").

RIA is an open-source tool built to solve a simple but annoying problem: Ratiometric analysis shouldn't be stuck on the microscope computer.

Ratiometric imaging (like FRET or sensors for Tryptophan/pH/Ca²⁺) is amazing for normalizing data, but analyzing it usually requires expensive commercial software (like MetaMorph or NIS-Elements) that is locked to a specific workstation with a dongle.

We built RIA so you can take your TIFF stacks, go to a coffee shop (or just your desk), and run rigorous analysis on your own laptop—no coding required.

[Image of fluorescence ratiometric imaging process diagram]

💡 Why use RIA?

• Analysis Unchained: Stop queuing for the lab workstation. RIA is a standalone executable that runs on standard PCs.
• Math Done Right: Calculating ratios isn't just A / B. Biological images have edges and noise. We implemented a normalized convolution algorithm that handles NaN (Not a Number) values correctly. This means your data doesn't get eroded or corrupted at cell boundaries—a common issue in simple script-based analysis.
• Zero Coding Needed: We know not everyone loves Python. RIA has a full GUI for background subtraction, thresholding, and dragging-and-dropping ROIs.
• Trust Your Data: We don't hide the numbers. You get the visual stacks, but you also get the raw float32 ratio data and time-series CSVs. You can take these straight to Prism, Origin, or Excel.

📁 Project Structure

RatioImagingAnalyzer/
├── data/               # Sample TIFFs so you can try it out immediately
├── paper/              # JOSS submission files
├── src/                # The actual code
│   ├── main.py         # Start here
│   ├── gui.py          # The frontend logic
│   ├── processing.py   # The math/algorithm heavy lifting
│   └── components.py   # UI Widgets
├── tests/              # Automated tests to keep bugs away
└── requirements.txt    # Dependencies

🚀 Installation

Option 1: Running from Source (Recommended for Developers/Reviewers)

1. Clone the repository:

   git clone https://github.com/Epivitae/RatioImagingAnalyzer.git
   cd RatioImagingAnalyzer
   

2. Install dependencies: It is recommended to use a virtual environment.

   pip install -r requirements.txt
   

3. Run the application: The source code is located in the src directory:

   python src/main.py
   

Option 2: Standalone Executable (For End Users)

Check the Releases page to download the latest compiled .exe file for Windows. No Python installation is required.

📖 Usage Example

To test the software, you can use the sample data provided in the data/ directory.

1. Launch RIA (python src/main.py).
2. Load Files:
   • Click 📂 Ch1 and select data/C1.tif.
   • Click 📂 Ch2 and select data/C2.tif.
   • Click 🚀 Load & Analyze.
3. Adjust Parameters:
   • Set BG % (Background Subtraction) to ~5-10%.
   • Adjust Int. Min (Intensity Threshold) to remove background noise.
   • (Optional) Enable Log Scale if the dynamic range is large.
4. Analyze:
   • Click ✏️ Draw ROI in the "ROI & Measurement" panel.
   • Draw a rectangle on the cell of interest.
   • A curve window will pop up showing the ratio change over time.

🧪 Testing

This project uses pytest to ensure algorithm accuracy. The tests are located in the tests/ directory.

To run the automated tests:

python -m pytest tests/

🤝 Contributing

Contributions are welcome! If you encounter any bugs or have feature requests, please check the Issue Tracker or submit a Pull Request.

📄 License

Distributed under the MIT License. See LICENSE for more information.

📚 References & Dependencies

This software relies on the following open-source libraries and methods:

• Methodology: Tao, R., Wang, K., et al. (2023). A genetically encoded ratiometric indicator for tryptophan. Cell Discovery, 9, 106. DOI: 10.1038/s41421-023-00608-1
• NumPy: Harris, C. R., et al. (2020). Array programming with NumPy. Nature, 585(7825), 357–362. DOI: 10.1038/s41586-020-2649-2
• SciPy: Virtanen, P., et al. (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17, 261–272. DOI: 10.1038/s41592-019-0686-2
• Matplotlib: Hunter, J. D. (2007). Matplotlib: A 2D graphics environment. Computing in Science & Engineering, 9(3), 90–95. DOI: 10.1109/MCSE.2007.55
• Tifffile: Gohlke, C. (2023). tifffile. PyPI. URL
• Fiji (Inspiration): Schindelin, J., et al. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676–682.

Citation

Welcome to use RIA, please cite:
Wang, K. (2025). Ratio Imaging Analyzer (RIA): A Lightweight, Standalone Python Tool for Portable Ratiometric Fluorescence Analysis (v1.7.9.1). Zenodo.