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archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems
Quantifying plant morphology is a very challenging task that requires methods able to capture the geometry and topology of plant organs at various spatial scales. Recently, the use of persistent homology as a mathematical framework to quantify plant morphology has been successfully demonstrated for...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5871803/ https://www.ncbi.nlm.nih.gov/pubmed/29636899 http://dx.doi.org/10.12688/f1000research.13541.1 |
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author | Delory, Benjamin M. Li, Mao Topp, Christopher N. Lobet, Guillaume |
author_facet | Delory, Benjamin M. Li, Mao Topp, Christopher N. Lobet, Guillaume |
author_sort | Delory, Benjamin M. |
collection | PubMed |
description | Quantifying plant morphology is a very challenging task that requires methods able to capture the geometry and topology of plant organs at various spatial scales. Recently, the use of persistent homology as a mathematical framework to quantify plant morphology has been successfully demonstrated for leaves, shoots, and root systems. In this paper, we present a new data analysis pipeline implemented in the R package archiDART to analyse root system architectures using persistent homology. In addition, we also show that both geometric and topological descriptors are necessary to accurately compare root systems and assess their natural complexity. |
format | Online Article Text |
id | pubmed-5871803 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | F1000 Research Limited |
record_format | MEDLINE/PubMed |
spelling | pubmed-58718032018-04-09 archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems Delory, Benjamin M. Li, Mao Topp, Christopher N. Lobet, Guillaume F1000Res Software Tool Article Quantifying plant morphology is a very challenging task that requires methods able to capture the geometry and topology of plant organs at various spatial scales. Recently, the use of persistent homology as a mathematical framework to quantify plant morphology has been successfully demonstrated for leaves, shoots, and root systems. In this paper, we present a new data analysis pipeline implemented in the R package archiDART to analyse root system architectures using persistent homology. In addition, we also show that both geometric and topological descriptors are necessary to accurately compare root systems and assess their natural complexity. F1000 Research Limited 2018-01-08 /pmc/articles/PMC5871803/ /pubmed/29636899 http://dx.doi.org/10.12688/f1000research.13541.1 Text en Copyright: © 2018 Delory BM et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Software Tool Article Delory, Benjamin M. Li, Mao Topp, Christopher N. Lobet, Guillaume archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems |
title | archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems |
title_full | archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems |
title_fullStr | archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems |
title_full_unstemmed | archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems |
title_short | archiDART v3.0: A new data analysis pipeline allowing the topological analysis of plant root systems |
title_sort | archidart v3.0: a new data analysis pipeline allowing the topological analysis of plant root systems |
topic | Software Tool Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5871803/ https://www.ncbi.nlm.nih.gov/pubmed/29636899 http://dx.doi.org/10.12688/f1000research.13541.1 |
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