FociPy is an open-source Python pipeline designed for the automated detection, segmentation, and quantification of protein accumulation at DNA damage sites. While specifically optimized for analyzing XRCC1 foci in response to light-induced DNA damage, the core architecture is modular and can be adapted to various nuclear markers.
It aims to provide a reproducible and objective alternative to manual foci counting, enabling high-throughput analysis of large microscopy datasets. This software is being developed as part of a master's thesis in the field of bioimage analysis and computational biology.
Warning
UNDER DEVELOPMENT - NOT YET USABLE
FociPy is currently under active development and is not intended for end users at this stage. The software is incomplete, subject to major changes, and the results may be unreliable. Do not use this tool for production or quantitative scientific analysis yet.
- Automated segmentation of cell nuclei from fluorescence microscopy images
- Detection and quantification of XRCC1 nuclear foci
- Analysis of XRCC1 accumulation as a function of applied light dose
- Batch processing of large image datasets
- Export of quantitative results for downstream statistical analysis
- Classical and deep-learning-based nuclear segmentation (e.g. Cellpose / StarDist)
- Robust XRCC1 foci detection using image processing methods
- Per-nucleus foci statistics (count, intensity, spatial distribution)
- Fully automated batch analysis pipeline
- Generation of publication-ready plots and tables
- Modular architecture allowing extension to other DNA damage markers
- Project stage: very early development
- Validation: not started
- Documentation: not started
The repository currently serves as a development and learning environment. Core algorithms, parameters, and workflows are yet to be defined.
This project is a research prototype developed for academic purposes. I make no guarantees regarding correctness, completeness, or suitability of the results at the current development stage.
Detailed documentation, including usage instructions (USAGE.md), methodology (METHODS.md), and changelogs (CHANGELOG.md), can be found in the docs/ directory.