DOI: 10.5061/dryad.rxwdbrv93
title: Measuring hidden phenotype: quantifying the shape of barley seeds using the Euler characteristic transform

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.
description: We selected 28 barley accessions with diverse spike morphologies
 and geographical origins for our analysis (Harlan and Martini 1929, 1936,
 1940). In November of 2016, seeds from each accession were stratified at
 4C on wet paper towels for a week, and germinated on the bench at room
 temperature. Four day old seedlings were transferred into pots in
 triplicate and arranged in a completely randomized design in a greenhouse.
 Day length was extended throughout the experiment using artificial
 lighting ---minimum 16h light / 8h dark. After the plants reached maturity
 and dried, a single spike was collected from each replicate for scanning
 at Michigan State University.The scans were produced using the North Star
 Imaging X3000 system and the included efX software, with 720 projections
 per scan, with 3 frames averaged per projection. The data was obtained in
 continuous mode. The X‐ray source was set to a voltage of 75 kV, current
 of 100 &mu;A, and focal spot size of 7.5&mu;m. The 3D
 reconstruction of the spikes was computed with the efX-CT software,
 obtaining a final voxel size of 127 microns. The intensity values for all
 raw reconstructions was standardized as a first step to guarantee that the
 air and the barley material had the same density values across all scans.
 Next, the air and debris were thresholded out, and awns
 digitally. Finally, the seed coat of the caryopses was
 digitally removed, leaving only the embryo and endosperm due to their high
 water content. We did not have enough resolution in the raw scans to
 distinguish clearly the endosperm from the embryo. Hereafter, we will
 refer to these embryo-endosperm unions simply as seeds. Due to the large
 volume of data, we used an in-house scipy-based python script to automate
 the image processing pipeline for all panicles and grains.
description: 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.