Title |
Topological Data Analysis as a Morphometric Method: Using Persistent Homology to Demarcate a Leaf Morphospace
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Published in |
Frontiers in Plant Science, April 2018
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DOI | 10.3389/fpls.2018.00553 |
Pubmed ID | |
Authors |
Mao Li, Hong An, Ruthie Angelovici, Clement Bagaza, Albert Batushansky, Lynn Clark, Viktoriya Coneva, Michael J. Donoghue, Erika Edwards, Diego Fajardo, Hui Fang, Margaret H. Frank, Timothy Gallaher, Sarah Gebken, Theresa Hill, Shelley Jansky, Baljinder Kaur, Phillip C. Klahs, Laura L. Klein, Vasu Kuraparthy, Jason Londo, Zoë Migicovsky, Allison Miller, Rebekah Mohn, Sean Myles, Wagner C. Otoni, J. C. Pires, Edmond Rieffer, Sam Schmerler, Elizabeth Spriggs, Christopher N. Topp, Allen Van Deynze, Kuang Zhang, Linglong Zhu, Braden M. Zink, Daniel H. Chitwood |
Abstract |
Current morphometric methods that comprehensively measure shape cannot compare the disparate leaf shapes found in seed plants and are sensitive to processing artifacts. We explore the use of persistent homology, a topological method applied as a filtration across simplicial complexes (or more simply, a method to measure topological features of spaces across different spatial resolutions), to overcome these limitations. The described method isolates subsets of shape features and measures the spatial relationship of neighboring pixel densities in a shape. We apply the method to the analysis of 182,707 leaves, both published and unpublished, representing 141 plant families collected from 75 sites throughout the world. By measuring leaves from throughout the seed plants using persistent homology, a defined morphospace comparing all leaves is demarcated. Clear differences in shape between major phylogenetic groups are detected and estimates of leaf shape diversity within plant families are made. The approach predicts plant family above chance. The application of a persistent homology method, using topological features, to measure leaf shape allows for a unified morphometric framework to measure plant form, including shapes, textures, patterns, and branching architectures. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 16 | 48% |
United Kingdom | 3 | 9% |
Canada | 1 | 3% |
Mexico | 1 | 3% |
Brazil | 1 | 3% |
Nepal | 1 | 3% |
Unknown | 10 | 30% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 17 | 52% |
Scientists | 16 | 48% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 113 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 23 | 20% |
Researcher | 22 | 19% |
Student > Master | 10 | 9% |
Student > Bachelor | 9 | 8% |
Student > Doctoral Student | 8 | 7% |
Other | 16 | 14% |
Unknown | 25 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 42 | 37% |
Biochemistry, Genetics and Molecular Biology | 15 | 13% |
Engineering | 7 | 6% |
Physics and Astronomy | 5 | 4% |
Computer Science | 3 | 3% |
Other | 15 | 13% |
Unknown | 26 | 23% |