poca-smlm.github.io
Documentation for the PoCA software platform
Science Score: 36.0%
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Repository
Documentation for the PoCA software platform
Basic Info
- Host: GitHub
- Owner: poca-SMLM
- Default Branch: main
- Size: 154 MB
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- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
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, 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, Rmi Galland, Corey Butler, Anne Beghin, Anal Chazeau, Philipp Hoess, Jonas Ries, Grgory 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
- Quantification techniques
- Objects
- Colocalization analysis
- ROIs
- Macros
- Executing Python scripts
- How to cite
Use cases
Owner
- Login: poca-SMLM
- Kind: user
- Repositories: 1
- Profile: https://github.com/poca-SMLM
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