Cell migration involves dynamic changes in cell shape. Intricate patterns of cell shape can be analyzed and classified using advanced shape descriptors, including spherical harmonics (SPHARM). Though SPHARM have been used to analyze and classify migrating cells, such classification did not exploit SPHARM spectra in their dynamics. Here, we examine whether additional information from dynamic SPHARM improves classification of cell migration patterns. We combine the static and dynamic SPHARM approach with a support-vector-machine classifier and compare their classification accuracies. We demonstrate that the dynamic SPHARM analysis classifies cell migration patterns more accurately than the static one for both synthetic and experimental data. Furthermore, by comparing the computed accuracies with that of a naive classifier, we can identify the experimental conditions and model parameters that significantly affect cell shape. This capability should – in the future – help to pinpoint factors that play an essential role in cell migration.

细胞迁移涉及细胞形状的动态变化。可以使用高级形状描述符（包括球谐函数（SPHARM））对复杂的细胞形状模式进行分析和分类。尽管SPHARM已用于分析和分类迁移细胞，但这种分类并未在动力学中利用SPHARM光谱。在这里，我们检查了来自动态SPHARM的其他信息是否可以改善细胞迁移模式的分类。我们将静态和动态SPHARM方法与支持向量机分类器结合起来，并比较它们的分类精度。我们证明，对于合成和实验数据，动态SPHARM分析对细胞迁移模式的分类均比静态模式更为准确。此外，通过将计算的精度与天真分类器的精度进行比较，我们可以确定会严重影响细胞形状的实验条件和模型参数。将来，此功能应有助于查明在细胞迁移中起关键作用的因素。