Gao Tingran, Yapuncich Gabriel S, Daubechies Ingrid, Mukherjee Sayan, Boyer Doug M
Department of Mathematics, Duke University, Durham, North Carolina.
Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, North Carolina.
Anat Rec (Hoboken). 2018 Apr;301(4):636-658. doi: 10.1002/ar.23700. Epub 2017 Nov 11.
Automated geometric morphometric methods are promising tools for shape analysis in comparative biology, improving researchers' abilities to quantify variation extensively (by permitting more specimens to be analyzed) and intensively (by characterizing shapes with greater fidelity). Although use of these methods has increased, published automated methods have some notable limitations: pairwise correspondences are frequently inaccurate and pairwise mappings are not globally consistent (i.e., they lack transitivity across the full sample). Here, we reassess the accuracy of published automated methods-cPDist (Boyer et al. Proc Nat Acad Sci 108 () 18221-18226) and auto3Dgm (Boyer et al.: Anat Rec 298 () 249-276)-and evaluate several modifications to these methods. We show that a substantial percentage of alignments and pairwise maps between specimens of dissimilar geometries were inaccurate in the study of Boyer et al. (Proc Nat Acad Sci 108 () 18221-18226), despite a taxonomically partitioned variance structure of continuous Procrustes distances. We show these inaccuracies are remedied using a globally informed methodology within a collection of shapes, rather than relying on pairwise comparisons (c.f. Boyer et al.: Anat Rec 298 () 249-276). Unfortunately, while global information generally enhances maps between dissimilar objects, it can degrade the quality of correspondences between similar objects due to the accumulation of numerical error. We explore a number of approaches to mitigate this degradation, quantify their performance, and compare the generated pairwise maps (and the shape space characterized by these maps) to a "ground truth" obtained from landmarks manually collected by geometric morphometricians. Novel methods both improve the quality of the pairwise correspondences relative to cPDist and achieve a taxonomic distinctiveness comparable to auto3Dgm. Anat Rec, 301:636-658, 2018. © 2017 Wiley Periodicals, Inc.
自动化几何形态测量方法是比较生物学中进行形状分析的有前途的工具,它提高了研究人员广泛(通过允许分析更多标本)和深入(通过更精确地描述形状)量化变异的能力。尽管这些方法的使用有所增加,但已发表的自动化方法存在一些显著局限性:成对对应经常不准确,且成对映射在全局上不一致(即它们在整个样本中缺乏传递性)。在这里,我们重新评估已发表的自动化方法——cPDist(Boyer等人,《美国国家科学院院刊》108( ) 18221 - 18226)和auto3Dgm(Boyer等人:《解剖学记录》298( ) 249 - 276)的准确性,并评估对这些方法的几种改进。我们表明,在Boyer等人(《美国国家科学院院刊》108( ) 18221 - 18226)的研究中,尽管连续普氏距离具有分类学划分的方差结构,但不同几何形状标本之间的大量对齐和成对映射是不准确的。我们表明,使用形状集合内的全局信息方法可以纠正这些不准确之处,而不是依赖成对比较(参见Boyer等人:《解剖学记录》298( ) 249 - 276)。不幸的是,虽然全局信息通常会增强不同对象之间的映射,但由于数值误差的积累,它可能会降低相似对象之间对应关系的质量。我们探索了多种方法来减轻这种退化,量化它们的性能,并将生成的成对映射(以及由这些映射表征的形状空间)与从几何形态测量学家手动收集的地标获得的“真实情况”进行比较。新方法相对于cPDist提高了成对对应的质量,并实现了与auto3Dgm相当的分类学独特性。《解剖学记录》,301:636 - 658,2018。© 2017威利期刊公司。