cell_curvature_analyzer
A comprehensive GUI application for analyzing the relationship between cell-membrane curvature and PIEZO1 protein locations from fluorescence microscope recordings.
Overview
Languages: Python
Documentation
Cell Curvature Analyzer
A comprehensive GUI application for analyzing the relationship between cell-membrane curvature and PIEZO1 protein locations from fluorescence microscope recordings.
Features
- Interactive Visualization: View original images, binary masks, and analysis overlays side-by-side
- Curvature Analysis: Calculate three types of curvature (sign, magnitude, and normalized)
- Intensity Correlation: Analyze the relationship between curvature and fluorescence intensity
- Temporal Analysis: Track changes across frames and correlate with previous frames
- Edge Movement Detection: Classify cell edge regions as extending, retracting, or stable
- Multi-tab Interface: Dedicated views for different analysis aspects
- Data Export: Comprehensive export options for images, data, and reports
Installation
Prerequisites
- Python 3.7 or higher
- NumPy, Matplotlib, SciPy
- scikit-image
- pandas
- PyQt5
- tifffile
Install from Source
# Clone the repository
git clone https://github.com/your-username/cell-curvature-analyzer.git
cd cell-curvature-analyzer
# Create and activate a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# Install the package
pip install -e .
Install from PyPI
Usage
Starting the Application
# If installed from source with -e flag
cell-curvature-analyzer
# Or run directly from the source directory
python cell_curvature_analyzer.py
Loading Data
- Click Open Images to load a microscope image stack (TIFF format)
- Click Open Masks to load corresponding binary masks (TIFF format)
- Set analysis parameters in the right panel
- Click Run Analysis to process all frames
Navigating the Interface
- Visualization Tab: View images and overlay analysis results
[View full README on GitHub]