Multiomics Tissue Segmentation via Spatially-Informed Nested Biclustering Methods
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914017420771328 |
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| author | Denti, Francesco Balocchi, Cecilia Denti, Vanna Capitoli, Giulia |
| author_facet | Denti, Francesco Balocchi, Cecilia Denti, Vanna Capitoli, Giulia |
| contents | Matrix-Assisted Laser Desorption/Ionisation Mass Spectrometry Imaging (MSI) is a powerful technique for spatially resolved molecular profiling and cancer biomarker discovery. Recent advances, including a novel multiomics workflow, enable multiple rounds of MSI on the same tissue section, extracting diverse molecular classes, e.g., lipids, peptides, and N-glycans, while preserving spatial resolution. This innovation is particularly valuable for studies with limited tissue sample availability, such as rare diseases or small tumors. However, the resulting data are high-dimensional, spatially structured, and the various molecular types share the same pixel grid. To address these challenges, we propose Poseidon, a Bayesian nonparametric nested biclustering model that simultaneously segments the common tissue pixels and clusters molecular signals within each molecular class, leveraging the shared spatial structure. A separately exchangeable framework is first considered, and then extended to handle spatial data via hidden Markov random fields. For scalability, we implement an efficient mean-field variational inference algorithm tailored for multi-dataset analysis. After validating the efficacy of our method on simulated scenarios, we demonstrate the applicability of our model in a real-world case study, where multiomics measurements were performed on kidney tissue affected by clear cell renal cell carcinoma. The nested, hierarchical structure of Poseidon, combined with its principled inferential framework, allows the extraction of interesting biological insights, such as clear tissue segmentation and biomarker detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_02482 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multiomics Tissue Segmentation via Spatially-Informed Nested Biclustering Methods Denti, Francesco Balocchi, Cecilia Denti, Vanna Capitoli, Giulia Methodology Matrix-Assisted Laser Desorption/Ionisation Mass Spectrometry Imaging (MSI) is a powerful technique for spatially resolved molecular profiling and cancer biomarker discovery. Recent advances, including a novel multiomics workflow, enable multiple rounds of MSI on the same tissue section, extracting diverse molecular classes, e.g., lipids, peptides, and N-glycans, while preserving spatial resolution. This innovation is particularly valuable for studies with limited tissue sample availability, such as rare diseases or small tumors. However, the resulting data are high-dimensional, spatially structured, and the various molecular types share the same pixel grid. To address these challenges, we propose Poseidon, a Bayesian nonparametric nested biclustering model that simultaneously segments the common tissue pixels and clusters molecular signals within each molecular class, leveraging the shared spatial structure. A separately exchangeable framework is first considered, and then extended to handle spatial data via hidden Markov random fields. For scalability, we implement an efficient mean-field variational inference algorithm tailored for multi-dataset analysis. After validating the efficacy of our method on simulated scenarios, we demonstrate the applicability of our model in a real-world case study, where multiomics measurements were performed on kidney tissue affected by clear cell renal cell carcinoma. The nested, hierarchical structure of Poseidon, combined with its principled inferential framework, allows the extraction of interesting biological insights, such as clear tissue segmentation and biomarker detection. |
| title | Multiomics Tissue Segmentation via Spatially-Informed Nested Biclustering Methods |
| topic | Methodology |
| url | https://arxiv.org/abs/2509.02482 |