PyCAT-Napari Documentation

PyCAT (Python Condensate Analysis Toolbox) is an open-source application built on napari for analyzing biomolecular condensates in biological images. It provides a comprehensive suite of tools for fluorescence image analysis, particularly focused on condensate detection, measurement, and characterization. PyCAT aims to provide a low/no-code solution, accessible to researchers with varying levels of programming experience.

Table of Contents

• Installation
  • System Requirements
  • Pre-Installation Setup
  • Installation Methods
  • Verification and Testing
  • Troubleshooting
  • Python and Package Manager Installation
• Usage
  • Usage Options
  • Available Tutorials
  • Getting Help
  • Contributing
• Features
  • Analysis Methods Available
  • Analysis Pipelines
  • Toolbox Reference
  • Cell Analysis Features
  • Puncta Analysis Features
• API Reference
  • pycat.data
  • pycat.file_io
  • pycat.toolbox
  • pycat.ui
  • pycat.utils
  • pycat.central_manager
  • pycat.run_pycat
• Development Guide
  • Getting Started with Development
  • Development Resources
  • Setting Up Development Environment
  • Key Development Tools
  • Development Guidelines Overview
  • Next Steps
• Contributing to PyCAT-Napari
  • Getting Started
  • Making Contributions
  • Pull Requests
  • Code Style and Standards
  • Documentation
  • Code of Conduct
  • Getting Help
  • Development Tips
• Getting Support
  • Quick Support Guide
  • Common Issues
  • Getting Help
  • Contributing to Support
  • Best Practices for Getting Help
  • Troubleshooting Guide
  • Future Support Plans
• PyCAT-Napari Roadmap
  • Core Functionalities
  • Performance Improvements
  • Advanced Analysis Tools
  • Batch & Video Processing
  • Machine Learning Integration
  • Data Management & Output
  • Miscellaneous Enhancements
  • Future Features & Research Integration
  • Documentation & User Support
  • Known Issues
  • Local Thresholding - Work In Progress (WIP)

Features

PyCAT-Napari offers a wide range of tools for biological image analysis, including but not limited to:

• Image Processing and Segmentation: Versatile toolbox with common image processing and analysis functions. Specialized condensate segmentation and object filtering algorithms. Optimized for in-cellulo analysis in challenging biological datasets.

• Quantitative Region Analysis: Simple and intuitive layer and ROI mask design. Extensive ROI feature analysis, including area, intensity, shape, texture, and more. Advanced colocalization analysis with object-based and pixel-wise methods.

• Integrated Analysis Pipelines:

  • Condensate Analysis Pipeline: Tailored for in-cellulo biomolecular condensates.

  • Colocalization Analysis Pipeline: Combines object-based and pixel-wise methods for robust colocalization studies.

  • General ROI Analysis Pipeline: Flexible pipeline for various region of interest analyses.

For a complete overview, see the Features page.

Installation

Follow the Installation guide to set up PyCAT-Napari on your system. It covers system requirements, installation methods, and verification steps.

Usage

PyCAT-Napari provides both a graphical user interface (GUI) and programmatic API access. The GUI is recommended for most users for an intuitive experience.

Explore our tutorials and usage guides in the Usage section.

Development

Interested in contributing or developing PyCAT-Napari further? Visit the Development Guide for information on setting up your development environment, guidelines, and more.

Contributing

We welcome contributions! Please see our Contributing to PyCAT-Napari for details on how to contribute to PyCAT-Napari.

Support

Need help or have questions? Check our Getting Support page for resources and troubleshooting assistance.

Roadmap

Interested in the future of PyCAT-Napari? View our PyCAT-Napari Roadmap to see planned features and improvements.

Indices and Tables

• Index

• Module Index

• Search Page