PoCA: Point Cloud Analyst

Introduction

PoCA is a a powerful stand-alone software designed to ease the manipulation and quantification of multidimensional and multicolor SMLM point cloud data. It is built around a custom-made Open-GL-based rendering engine that provides full user interactive control of SMLM point cloud data, both for visualization and manipulation. It combines the strengths of both C++ and Python programming languages, providing access to efficient and optimized C++ computer graphics algorithms and Python ecosystem. It is designed for improving users and developers’ experience, by integrating a user-friendly GUI, a macro recorder, and the capability to execute Python code easily. PoCA is the result of a decade of developments and the legacy of SR-Tesseler and Coloc-Tesseler, software solutions that were swiftly adopted by the community.

If you use it, please cite it:

• Florian Levet & Jean-Baptiste Sibarita. PoCA: a software platform for point cloud data visualization and quantification. Nature Methods 20, 629–630 (2023) doi:10.1038/s41592-023-01811-4

• Florian Levet, Eric Hosy, Adel Kechkar, Corey Butler, Anne Beghin, Daniel Choquet, Jean-Baptiste Sibarita. SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data. Nature Methods 12 (11), 1065-71 (2015) doi:10.1038/nmeth.3579

• Florian Levet, Guillaume Julien, Rémi Galland, Corey Butler, Anne Beghin, Anaël Chazeau, Philipp Hoess, Jonas Ries, Grégory Giannone, Jean-Baptiste Sibarita. A tessellation-based colocalization analysis approach for single-molecule localization microscopy. Nature Communications 10, 2379 (2019) doi:10.1038/s41467-019-10007-4

PoCA is developed by Florian Levet, researcher in the Quantitative Imaging of the Cell team, headed by Jean-Baptiste Sibarita. FL and JBS are part of the Interdisciplinary Insitute for Neuroscience. FL is part of the Bordeaux Imaging Center.

If you search for support, please open a thread on the image.sc forum or raise an issue here.

Overview

• Installation and compilation
• PoCA main interface
• Opening localization files
• Manipulating point clouds
  • Visualization
  • Filtering
  • Cropping
  • Picking
• Quantification techniques
  • Delaunay triangulation
  • Voronoi diagram
  • DBSCAN
• Objects
• Colocalization analysis
  • Prerequesite
  • Coloc-Tesseler
  • Object colocalization
• ROIs
• Macros
• Executing Python scripts
• How to cite

Use cases

• Creation of clusters with a Voronoi diagram
• Creation of clusters with a Delaunay triangulation
• Colocalization analysis with Coloc-Tesseler
• Colocalization analysis with object colocalization