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  • Accepted Manuscript: Measuring hidden phenotype: quantifying the shape of barley seeds using the Euler characteristic transform
Title: Measuring hidden phenotype: quantifying the shape of barley seeds using the Euler characteristic transform
Abstract Shape plays a fundamental role in biology. Traditional phenotypic analysis methods measure some features but fail to measure the information embedded in shape comprehensively. To extract, compare and analyse this information embedded in a robust and concise way, we turn to topological data analysis (TDA), specifically the Euler characteristic transform. TDA measures shape comprehensively using mathematical representations based on algebraic topology features. To study its use, we compute both traditional and topological shape descriptors to quantify the morphology of 3121 barley seeds scanned with X-ray computed tomography (CT) technology at 127 μm resolution. The Euler characteristic transform measures shape by analysing topological features of an object at thresholds across a number of directional axes. A Kruskal–Wallis analysis of the information encoded by the topological signature reveals that the Euler characteristic transform picks up successfully the shape of the crease and bottom of the seeds. Moreover, while traditional shape descriptors can cluster the seeds based on their accession, topological shape descriptors can cluster them further based on their panicle. We then successfully train a support vector machine to classify 28 different accessions of barley based exclusively on the shape of their grains. We observe that combining both traditional and topological more » descriptors classifies barley seeds better than using just traditional descriptors alone. This improvement suggests that TDA is thus a powerful complement to traditional morphometrics to comprehensively describe a multitude of ‘hidden’ shape nuances which are otherwise not detected. « less
Authors:
; ; ; ; ; ;
Editors:
Chen, Tsu-Wei; Long, Stephen P
Award ID(s):
2018432 2046256
Publication Date:
NSF-PAR ID:
10358277
Journal Name:
in silico Plants
Volume:
4
Issue:
1
ISSN:
2517-5025
Sponsoring Org:
National Science Foundation
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