Temporal data, obtained in the setting where it is only possible to observe one time point per trajectory, is widely used in different research fields, yet remains insufficiently addressed from the statistical point of view. Such data often contain observations of a large number of entities, in which case it is of interest to identify a small number of representative behavior types. In this paper, we propose a new method performing clustering simultaneously with alignment of temporal objects inferred from these data, providing insight into the relationships between the entities. A series of simulations confirm the ability of the proposed approach to leverage multiple properties of the complex data we target such as accessible uncertainties, correlations and a small number of time points. We illustrate it on real data encoding cellular response to a radiation treatment with high energy, supported with the results of an enrichment analysis.

翻译：在仅能观测每条轨迹中一个时间点的场景下获取的时序数据，广泛存在于不同研究领域，但统计学视角对其关注仍显不足。此类数据通常包含大量实体的观测记录，如何从中识别出少量代表性行为类型成为关键问题。本文提出一种新方法，可在对数据推断出的时序对象进行对齐的同时完成聚类分析，为揭示实体间关联提供洞见。系列仿真实验表明，该方法能有效利用所关注复杂数据的多重特性，包括不确定性可量化性、相关性以及有限时间点特征。我们通过编码细胞对高能辐射治疗响应的真实数据验证了该方法，并结合富集分析结果进行了展示。