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A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D Shapes

View ORCID ProfileBruce Wang, View ORCID ProfileTimothy Sudijono, Henry Kirveslahti, View ORCID ProfileTingran Gao, Douglas M. Boyer, View ORCID ProfileSayan Mukherjee, View ORCID ProfileLorin Crawford
doi: https://doi.org/10.1101/701391
Bruce Wang
1Data Science Initiative, Brown University, Providence, RI, USA
2Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
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  • ORCID record for Bruce Wang
Timothy Sudijono
3Division of Applied Mathematics, Brown University, Providence, RI, USA
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Henry Kirveslahti
4Department of Statistical Science, Duke University, Durham, NC, USA
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Tingran Gao
5Committee on Computational and Applied Mathematics, Department of Statistics, University of Chicago, Chicago, IL, USA
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Douglas M. Boyer
6Department of Evolutionary Anthropology, Duke University, Durham, NC, USA
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Sayan Mukherjee
4Department of Statistical Science, Duke University, Durham, NC, USA
7Department of Computer Science, Duke University, Durham, NC, USA
8Department of Mathematics, Duke University, Durham, NC, USA
9Department of Bioinformatics & Biostatistics, Duke University, Durham, NC, USA
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Lorin Crawford
10Department of Biostatistics, Brown University, Providence, RI, USA
11Center for Statistical Sciences, Brown University, Providence, RI, USA
12Center for Computational Molecular Biology, Brown University, Providence, RI, USA
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  • For correspondence: lorin_crawford@brown.edu
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Abstract

The recent curation of large-scale databases with 3D surface scans of shapes has motivated the development of tools that better detect global-patterns in morphological variation. Studies which focus on identifying differences between shapes have been limited to simple pairwise comparisons and rely on pre-specified landmarks (that are often known). We present SINATRA: the first statistical pipeline for analyzing collections of shapes without requiring any correspondences. Our novel algorithm takes in two classes of shapes and highlights the physical features that best describe the variation between them. We use a rigorous simulation framework to assess our approach. Lastly, as a case study, we use SINATRA to analyze mandibular molars from four different suborders of primates and demonstrate its ability recover known morphometric variation across phylogenies.

Footnotes

  • https://github.com/lcrawlab/SINATRA

  • http://www.wisdom.weizmann.ac.il/~ylipman/CPsurfcomp/

  • https://gaotingran.com/codes/codes.html

  • http://morphosource.org/Detail/ProjectDetail/Show/project_id/89

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 03, 2019.
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A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D Shapes
Bruce Wang, Timothy Sudijono, Henry Kirveslahti, Tingran Gao, Douglas M. Boyer, Sayan Mukherjee, Lorin Crawford
bioRxiv 701391; doi: https://doi.org/10.1101/701391
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A Statistical Pipeline for Identifying Physical Features that Differentiate Classes of 3D Shapes
Bruce Wang, Timothy Sudijono, Henry Kirveslahti, Tingran Gao, Douglas M. Boyer, Sayan Mukherjee, Lorin Crawford
bioRxiv 701391; doi: https://doi.org/10.1101/701391

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