Measuring hidden phenotype: quantifying the shape of barley seeds using the Euler characteristic transform
Measuring hidden phenotype: quantifying the shape of barley seeds using the Euler characteristic transform
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DOI：
10.1093/insilicoplants/diab033
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发表时间：
2022-01-01
期刊：
影响因子：
3.1
通讯作者：
Chitwood, Daniel H.
中科院分区：
文献类型：
--
作者：
Amezquita, Erik J.;Quigley, Michelle Y.;Chitwood, Daniel H.
形状在生物学中起着重要作用。传统的表型分析方法测量了一些特征，但不能全面测量形状中嵌入的信息。为了以稳健和简洁的方式提取、比较和分析这些嵌入的信息，我们转向拓扑数据分析（TDA），特别是欧拉特征变换。TDA使用基于代数拓扑特征的数学表示全面测量形状。为了研究它的用途，我们计算了传统和拓扑形状描述符，量化了3121颗大麦种子的形态，这些种子是用x射线计算机断层扫描（CT）技术在127 μ m分辨率下扫描的。欧拉特征变换通过在多个方向轴的阈值处分析对象的拓扑特征来测量形状。对拓扑特征编码的信息进行Kruskal-Wallis分析表明，欧拉特征变换成功地提取了种子的折痕和底部的形状。此外，传统的形状描述符可以根据种子的加入程度对种子进行聚类，而拓扑形状描述符可以根据种子的穗形进一步对种子进行聚类。然后，我们成功地训练了一个支持向量机，根据颗粒的形状对28种不同的大麦进行分类。我们观察到结合传统描述符和拓扑描述符比单独使用传统描述符更好地分类大麦种子。这一改进表明，TDA因此是传统形态测量学的有力补充，可以全面描述大量“隐藏”的形状细微差别，否则无法检测到。
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 mu m 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.