GeNePy3D: a quantitative geometry python toolbox for bioimaging [version 2; peer review: 2 approved]
The advent of large-scale fluorescence and electronic microscopy techniques along with maturing image analysis is giving life sciences a deluge of geometrical objects in 2D/3D(+t) to deal with. These objects take the form of large scale, localised, precise, single cell, quantitative data such as cel...
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| Published in | F1000 research Vol. 9; p. 1374 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
England
Faculty of 1000 Ltd
2020
Faculty of 1000 F1000 Research Limited F1000 Research Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2046-1402 2046-1402 |
| DOI | 10.12688/f1000research.27395.2 |
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| Abstract | The advent of large-scale fluorescence and electronic microscopy techniques along with maturing image analysis is giving life sciences a deluge of geometrical objects in 2D/3D(+t) to deal with. These objects take the form of large scale, localised, precise, single cell, quantitative data such as cells' positions, shapes, trajectories or lineages, axon traces in whole brains atlases or varied intracellular protein localisations, often in multiple experimental conditions. The data mining of those geometrical objects requires a variety of mathematical and computational tools of diverse accessibility and complexity. Here we present a new Python library for quantitative 3D geometry called GeNePy3D which helps handle and mine information and knowledge from geometric data, providing a unified application programming interface (API) to methods from several domains including computational geometry, scale space methods or spatial statistics. By framing this library as generically as possible, and by linking it to as many state-of-the-art reference algorithms and projects as needed, we help render those often specialist methods accessible to a larger community. We exemplify the usefulness of the GeNePy3D toolbox by re-analysing a recently published whole-brain zebrafish neuronal atlas, with other applications and examples available online. Along with an open source, documented and exemplified code, we release reusable containers to allow for convenient and wide usability and increased reproducibility. |
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| AbstractList | The advent of large-scale fluorescence and electronic microscopy techniques along with maturing image analysis is giving life sciences a deluge of geometrical objects in 2D/3D(+t) to deal with. These objects take the form of large scale, localised, precise, single cell, quantitative data such as cells' positions, shapes, trajectories or lineages, axon traces in whole brains atlases or varied intracellular protein localisations, often in multiple experimental conditions. The data mining of those geometrical objects requires a variety of mathematical and computational tools of diverse accessibility and complexity. Here we present a new Python library for quantitative 3D geometry called GeNePy3D which helps handle and mine information and knowledge from geometric data, providing a unified application programming interface (API) to methods from several domains including computational geometry, scale space methods or spatial statistics. By framing this library as generically as possible, and by linking it to as many state-of-the-art reference algorithms and projects as needed, we help render those often specialist methods accessible to a larger community. We exemplify the usefulness of the GeNePy3D toolbox by re-analysing a recently published whole-brain zebrafish neuronal atlas, with other applications and examples available online. Along with an open source, documented and exemplified code, we release reusable containers to allow for convenient and wide usability and increased reproducibility. The advent of large-scale fluorescence and electronic microscopy techniques along with maturing image analysis is giving life sciences a deluge of geometrical objects in 2D/3D(+t) to deal with. These objects take the form of large scale, localised, precise, single cell, quantitative data such as cells' positions, shapes, trajectories or lineages, axon traces in whole brains atlases or varied intracellular protein localisations, often in multiple experimental conditions. The data mining of those geometrical objects requires a variety of mathematical and computational tools of diverse accessibility and complexity. Here we present a new Python library for quantitative 3D geometry called GeNePy3D which helps handle and mine information and knowledge from geometric data, providing a unified application programming interface (API) to methods from several domains including computational geometry, scale space methods or spatial statistics. By framing this library as generically as possible, and by linking it to as many state-of-the-art reference algorithms and projects as needed, we help render those often specialist methods accessible to a larger community. We exemplify the usefulness of the GeNePy3D toolbox by re-analysing a recently published whole-brain zebrafish neuronal atlas, with other applications and examples available online. Along with an open source, documented and exemplified code, we release reusable containers to allow for convenient and wide usability and increased reproducibility.The advent of large-scale fluorescence and electronic microscopy techniques along with maturing image analysis is giving life sciences a deluge of geometrical objects in 2D/3D(+t) to deal with. These objects take the form of large scale, localised, precise, single cell, quantitative data such as cells' positions, shapes, trajectories or lineages, axon traces in whole brains atlases or varied intracellular protein localisations, often in multiple experimental conditions. The data mining of those geometrical objects requires a variety of mathematical and computational tools of diverse accessibility and complexity. Here we present a new Python library for quantitative 3D geometry called GeNePy3D which helps handle and mine information and knowledge from geometric data, providing a unified application programming interface (API) to methods from several domains including computational geometry, scale space methods or spatial statistics. By framing this library as generically as possible, and by linking it to as many state-of-the-art reference algorithms and projects as needed, we help render those often specialist methods accessible to a larger community. We exemplify the usefulness of the GeNePy3D toolbox by re-analysing a recently published whole-brain zebrafish neuronal atlas, with other applications and examples available online. Along with an open source, documented and exemplified code, we release reusable containers to allow for convenient and wide usability and increased reproducibility. The advent of large-scale fluorescence and electronic microscopy techniques along with maturing image analysis is giving life sciences a deluge of geometrical objects in 2D/3D(+t) to deal with. These objects take the form of large scale, localised, precise, single cell, quantitative data such as cells’ positions, shapes, trajectories or lineages, axon traces in whole brains atlases or varied intracellular protein localisations, often in multiple experimental conditions. The data mining of those geometrical objects requires a variety of mathematical and computational tools of diverse accessibility and complexity. Here we present a new Python library for quantitative 3D geometry called GeNePy3D which helps handle and mine information and knowledge from geometric data, providing a unified application programming interface (API) to methods from several domains including computational geometry, scale space methods or spatial statistics. By framing this library as generically as possible, and by linking it to as many state-of-the-art reference algorithms and projects as needed, we help render those often specialist methods accessible to a larger community. We exemplify the usefulness of the GeNePy3D toolbox by re-analysing a recently published whole-brain zebrafish neuronal atlas, with other applications and examples available online. Along with an open source, documented and exemplified code, we release reusable containers to allow for convenient and wide usability and increased reproducibility. |
| Author | Phan, Minh-Son Chessel, Anatole |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34249350$$D View this record in MEDLINE/PubMed https://pasteur.hal.science/pasteur-03678857$$DView record in HAL |
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| Cites_doi | 10.1038/s41467-018-03053-x 10.1038/s41592-019-0403-1 10.1101/2020.06.01.127035 10.1038/s41467-019-09337-0 10.18637/jss.v012.i06 10.1038/s41592-019-0686-2 10.1109/ISBI.2011.5872395 10.1016/j.neuron.2019.04.034 10.7554/eLife.53350 10.1038/nmeth.2019 10.1109/ISBI.2012.6235918 |
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| Copyright | Copyright: © 2021 Phan MS and Chessel A Copyright: © 2021 Phan MS and Chessel A. Copyright: © 2021 Phan MS and Chessel A. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Attribution Copyright: © 2021 Phan MS and Chessel A 2021 |
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| Keywords | computational geometry python bioimage informatics quantitative geometry workflow |
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| References | Dawson-Haggerty (ref-11) 2019 A Chessel (ref-4) 2009 P Virtanen (ref-9) 2020; 17 J Schindelin (ref-12) 2012; 9 Y Kashiwagi (ref-5) 2019; 10 F de Chaumont (ref-13) 2011 M Phan (ref-14) 2020 (ref-8) 2020 T Lagache (ref-3) 2018; 9 (ref-10) 2016 A Bates (ref-6) 2020; 9 M Phan (ref-15) 2020 A Baddeley (ref-7) 2005; 12 M Kunst (ref-17) 2019; 103 E Moen (ref-1) 2019; 16 A Chessel (ref-2) 2012 M Phan (ref-16) 2020 |
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| Snippet | The advent of large-scale fluorescence and electronic microscopy techniques along with maturing image analysis is giving life sciences a deluge of geometrical... |
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| SubjectTerms | Algorithms Animals Bioinformatics Brain Computational neuroscience Computer Science Data analysis Datasets Decomposition Geometry Image processing Image Processing, Computer-Assisted Informatics Libraries Machine learning Mathematics Reproducibility of Results Software Software Tool Statistical analysis Zebrafish Zebrafish - genetics |
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| Title | GeNePy3D: a quantitative geometry python toolbox for bioimaging [version 2; peer review: 2 approved] |
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