  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.
