Joint clustering with alignment for temporal data in a one-point-per-experiment setting
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866915185502978048 |
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| author | Arsenteva, Polina Benadjaoud, Mohamed Amine Cardot, Hervé |
| author_facet | Arsenteva, Polina Benadjaoud, Mohamed Amine Cardot, Hervé |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2311_10282 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Joint clustering with alignment for temporal data in a one-point-per-experiment setting Arsenteva, Polina Benadjaoud, Mohamed Amine Cardot, Hervé Methodology 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. |
| title | Joint clustering with alignment for temporal data in a one-point-per-experiment setting |
| topic | Methodology |
| url | https://arxiv.org/abs/2311.10282 |