Published May 31, 2023 | Version v1
Dataset Open

Measuring hidden phenotype: quantifying the shape of barley seeds using the Euler characteristic transform

  • 1. Michigan State University
  • 2. Eindhoven University of Technology
  • 3. Cornell University
  • 4. University of California, Riverside

Description

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 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.

Notes

X-ray CT scan images are provided for 774 individual barley panicle and their corresponding 37 881 clean, individual seeds. All the scans are provided as single 3D 8-bit TIFF files. Three raw X-ray CT scans containing 4 barley panicles each are included as well. The code developed to process these scans and segment out the panicles and seeds is provided as well. The code is written as commented jupyter notebooks, using both python and R.

Please read the README files included with the data for more details.

Funding provided by: National Institute of Food and Agriculture
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100005825
Award Number:

Funding provided by: AgBioResearch, Michigan State University
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100011138
Award Number:

Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: CCF-1907591

Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: CCF-2106578

Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: 1711807: Plant Genome Postdoctoral Fellowship

Funding provided by: National Science Foundation
Crossref Funder Registry ID: http://dx.doi.org/10.13039/100000001
Award Number: IOS-2046256: Plant Genome Research Program

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