Joint Clustering With Alignment for Temporal Data in a One-Point-per-Experiment Setting
提出一种同时进行时序对象聚类与对齐的新方法，适用于每实验仅观测一个时间点的数据，能处理不确定性、相关性和少量时间点，并在细胞辐射响应数据上验证。
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.