Department of Anthropology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
Department of Electrical and Compute Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America.
PLoS Comput Biol. 2023 Jan 19;19(1):e1009061. doi: 10.1371/journal.pcbi.1009061. eCollection 2023 Jan.
The methods of geometric morphometrics are commonly used to quantify morphology in a broad range of biological sciences. The application of these methods to large datasets is constrained by manual landmark placement limiting the number of landmarks and introducing observer bias. To move the field forward, we need to automate morphological phenotyping in ways that capture comprehensive representations of morphological variation with minimal observer bias. Here, we present Morphological Variation Quantifier (morphVQ), a shape analysis pipeline for quantifying, analyzing, and exploring shape variation in the functional domain. morphVQ uses descriptor learning to estimate the functional correspondence between whole triangular meshes in lieu of landmark configurations. With functional maps between pairs of specimens in a dataset we can analyze and explore shape variation. morphVQ uses Consistent ZoomOut refinement to improve these functional maps and produce a new representation of shape variation, area-based and conformal (angular) latent shape space differences (LSSDs). We compare this new representation of shape variation to shape variables obtained via manual digitization and auto3DGM, an existing approach to automated morphological phenotyping. We find that LSSDs compare favorably to modern 3DGM and auto3DGM while being more computationally efficient. By characterizing whole surfaces, our method incorporates more morphological detail in shape analysis. We can classify known biological groupings, such as Genus affiliation with comparable accuracy. The shape spaces produced by our method are similar to those produced by modern 3DGM and to auto3DGM, and distinctiveness functions derived from LSSDs show us how shape variation differs between groups. morphVQ can capture shape in an automated fashion while avoiding the limitations of manually digitized landmarks, and thus represents a novel and computationally efficient addition to the geometric morphometrics toolkit.
几何形态测量学方法常用于量化广泛的生物学科学中的形态。这些方法在大型数据集上的应用受到手动地标放置的限制，限制了地标数量并引入了观察者偏见。为了推动该领域的发展，我们需要以最小化观察者偏见的方式自动进行形态表型分析，以捕捉形态变化的综合表示。在这里，我们提出了 Morphological Variation Quantifier（morphVQ），这是一种用于量化、分析和探索功能域中形态变化的形状分析管道。morphVQ 使用描述符学习来估计整个三角形网格之间的功能对应关系，而不是地标配置。通过在数据集的一对标本之间建立功能映射，我们可以分析和探索形状变化。morphVQ 使用一致的 ZoomOut 细化来改进这些功能映射，并生成形状变化的新表示形式，基于面积和共形（角度）潜在形状空间差异（LSSD）。我们将这种新的形状变化表示与通过手动数字化和自动 3DGM 获得的形状变量进行比较，自动形态表型分析的现有方法。我们发现，LSSD 与现代 3DGM 和 auto3DGM 相比具有优势，同时计算效率更高。通过对整个表面进行特征化，我们的方法在形状分析中包含了更多的形态细节。我们可以以类似的准确性对已知的生物学分组进行分类，例如属的归属。我们的方法生成的形状空间与现代 3DGM 和 auto3DGM 生成的形状空间相似，并且从 LSSD 导出的区分函数可以帮助我们了解形状变化在各组之间的差异。morphVQ 可以以自动化的方式捕捉形状，同时避免手动数字化地标带来的限制，因此是几何形态测量学工具包的一种新颖且计算高效的补充。
