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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2504.02693 |
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| _version_ | 1866912307588628480 |
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| author | Eliason, Joel Rao, Arvind Frankel, Timothy L Peruzzi, Michele |
| author_facet | Eliason, Joel Rao, Arvind Frankel, Timothy L Peruzzi, Michele |
| contents | The tumor microenvironment (TME) is a spatially heterogeneous ecosystem where cellular interactions shape tumor progression and response to therapy. Multiplexed imaging technologies enable high-resolution spatial characterization of the TME, yet statistical methods for analyzing multi-subject spatial tissue data remain limited. We propose a Bayesian hierarchical model for inferring spatial dependencies in multiplexed imaging datasets across multiple subjects. Our model represents the TME as a multivariate log-Gaussian Cox process, where spatial intensity functions of different cell types are governed by a latent multivariate Gaussian process. By pooling information across subjects, we estimate spatial correlation functions that capture within-type and cross-type dependencies, enabling interpretable inference about disease-specific cellular organization. We validate our method using simulations, demonstrating robustness to latent factor specification and spatial resolution. We apply our approach to two multiplexed imaging datasets: pancreatic cancer and colorectal cancer, revealing distinct spatial organization patterns across disease subtypes and highlighting tumor-immune interactions that differentiate immune-permissive and immune-exclusive microenvironments. These findings provide insight into mechanisms of immune evasion and may inform novel therapeutic strategies. Our approach offers a principled framework for modeling spatial dependencies in multi-subject data, with broader applicability to spatially resolved omics and imaging studies. An R package, available online, implements our methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02693 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Joint Modeling of Spatial Dependencies Across Multiple Subjects in Multiplexed Tissue Imaging Eliason, Joel Rao, Arvind Frankel, Timothy L Peruzzi, Michele Applications The tumor microenvironment (TME) is a spatially heterogeneous ecosystem where cellular interactions shape tumor progression and response to therapy. Multiplexed imaging technologies enable high-resolution spatial characterization of the TME, yet statistical methods for analyzing multi-subject spatial tissue data remain limited. We propose a Bayesian hierarchical model for inferring spatial dependencies in multiplexed imaging datasets across multiple subjects. Our model represents the TME as a multivariate log-Gaussian Cox process, where spatial intensity functions of different cell types are governed by a latent multivariate Gaussian process. By pooling information across subjects, we estimate spatial correlation functions that capture within-type and cross-type dependencies, enabling interpretable inference about disease-specific cellular organization. We validate our method using simulations, demonstrating robustness to latent factor specification and spatial resolution. We apply our approach to two multiplexed imaging datasets: pancreatic cancer and colorectal cancer, revealing distinct spatial organization patterns across disease subtypes and highlighting tumor-immune interactions that differentiate immune-permissive and immune-exclusive microenvironments. These findings provide insight into mechanisms of immune evasion and may inform novel therapeutic strategies. Our approach offers a principled framework for modeling spatial dependencies in multi-subject data, with broader applicability to spatially resolved omics and imaging studies. An R package, available online, implements our methods. |
| title | Joint Modeling of Spatial Dependencies Across Multiple Subjects in Multiplexed Tissue Imaging |
| topic | Applications |
| url | https://arxiv.org/abs/2504.02693 |