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PoCA: a software platform for point cloud data visualization and quantification

Nature Methods volume 20, pages 629–630 (2023)Cite this article

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The PoCA graphical user interface (GUI) is simple, intuitive and user friendly (Fig. 1b). It is designed to allow users as well as developers to perform efficient investigation and quantitative analysis of SMLM data on a standard desktop computer equipped with a graphics processing unit (GPU). Every action or plot is available in a few mouse clicks, improving user efficiency and visual feedback. Filtering, object creation, data interaction and quantifications are examples of available native functionalities. Each computed quantitative feature (for example, localization density or object characteristics) can be displayed to improve visual feedback. PoCA also integrates capabilities such as file drag and drop, camera saving or look-up-tables.

PoCA is built around a custom Open-GL-based rendering engine that provides full user interactive control of SMLM point cloud data. It integrates several advanced visualization modes made to improve data comprehension. This includes uncertainty-based Gaussian intensity rendering (Fig. 1c), screen space ambient occlusion (Fig. 1d) and heat maps. 3D raw and segmented data can be manipulated for deeper investigations, thanks to annotated object bounding boxes and data cropping (Fig. 1c).

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Fig. 1: Overview of PoCA.

Code availability

PoCA is packaged as a one-click installer for the Windows operating system. The source code is available on GitHub (https://github.com/flevet/PoCA) under a LGPL v3 license. We provide a cmake script to facilitate its building inside other operating systems. The documentation is available at https://poca-smlm.github.io/.

References

  1. Ries, J. Nat. Methods 17, 870–872 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Marin, Z. et al. Nat. Methods 18, 582–584 (2021).

    Article  CAS  PubMed  Google Scholar 

  3. Levet, F. et al. Nat. Methods 12, 1065–1071 (2015).

  4. Levet, F. et al. Nat. Commun. 10, 2379 (2019).

  5. Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. In Proc. Second Int. Conf. Knowledge Discovery and Data Mining (KDD’96) 226–231 (AAAI Press, 1996).

  6. Harris, C. R. et al. Nature 585, 357–362 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Williamson, D. J. et al. Nat. Commun. 11, 1493 (2020).

  8. Berman, H. M. et al. Nucleic Acids Res. 28, 235–242 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Jungmann, R. et al. Nat. Methods 13, 439–442 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This work was supported by the Ministère de l’Enseignement Supérieur et de la Recherche (ANR-20-CE11-0006 NANO-SYNATLAS to F.L.), the Centre National de la Recherche Scientifique (CNRS), the Conseil Régional d’Aquitaine and the Institut National de la Santé et de la Recherche Médicale (Inserm). We also acknowledge France-BioImaging infrastructure supported by the French National Research Agency (ANR-10-INBS-04).

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Authors and Affiliations

  1. Univ. Bordeaux, CNRS, Interdisciplinary Institute for Neuroscience, Bordeaux, France

    Florian Levet & Jean-Baptiste Sibarita

  2. Univ. Bordeaux, CNRS, INSERM, Bordeaux Imaging Center, UAR3420, US 4, Bordeaux, France

    Florian Levet

Authors
  1. Florian Levet
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  2. Jean-Baptiste Sibarita
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Contributions

F.L. originated the idea and developed the software. F.L. and J.-B.S. designed the software requirements and contributed to the manuscript.

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Correspondence to Florian Levet or Jean-Baptiste Sibarita.

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The authors declare no competing interests.

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Nature Methods thanks Ann Wheeler and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Note and Supplementary Fig. 1

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Levet, F., Sibarita, JB. PoCA: a software platform for point cloud data visualization and quantification. Nat Methods 20, 629–630 (2023). https://doi.org/10.1038/s41592-023-01811-4

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