Temporal data, obtained in the setting where it is only possible to observe one time point per experiment, 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 that simultaneously performs clustering and alignment of temporal objects inferred from these data, providing insight into the relationships between entities. 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：在单点实验设置下获得的时序数据，即每个实验仅能观测一个时间点的数据，广泛应用于不同研究领域，但从统计学角度仍未得到充分研究。此类数据通常包含大量实体的观测值，因此识别少量代表性行为类型具有重要意义。本文提出一种新方法，能够同时对从这些数据推断出的时序对象进行聚类与对齐，从而揭示实体间的关联关系。仿真实验证实了所提方法能够有效利用目标复杂数据的多种特性，包括可获取的不确定性、相关性以及少量时间点等。我们通过编码细胞对高能辐射治疗响应的真实数据进行了方法验证，并辅以富集分析结果予以佐证。