SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918338924380160 |
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| author | Kong, Zhenglun Qiu, Mufan Boesen, John Lin, Xiang Yun, Sukwon Chen, Tianlong Kellis, Manolis Zitnik, Marinka |
| author_facet | Kong, Zhenglun Qiu, Mufan Boesen, John Lin, Xiang Yun, Sukwon Chen, Tianlong Kellis, Manolis Zitnik, Marinka |
| contents | Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology. Image-based spatial transcriptomics technologies now provide high-resolution measurements of cell images and gene expression profiles, but existing methods typically analyze these modalities in isolation or at limited resolution. We address the problem by introducing SPATIA, a multi-level generative and predictive model that learns unified, spatially aware representations by fusing morphology, gene expression, and spatial context from the cell to the tissue level. SPATIA also incorporates a novel spatially conditioned generative framework for predicting cell morphologies under perturbations. Specifically, we propose a confidence-aware flow matching objective that reweights weak optimal-transport pairs based on uncertainty. We further apply morphology-profile alignment to encourage biologically meaningful image generation, enabling the modeling of microenvironment-dependent phenotypic transitions. We assembled a multi-scale dataset consisting of 25.9 million cell-gene pairs across 17 tissues. We benchmark SPATIA against 18 models across 12 tasks, spanning categories such as phenotype generation, annotation, clustering, gene imputation, and cross-modal prediction. SPATIA achieves improved performance over state-of-the-art models, improving generative fidelity by 8% and predictive accuracy by up to 3%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04704 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes Kong, Zhenglun Qiu, Mufan Boesen, John Lin, Xiang Yun, Sukwon Chen, Tianlong Kellis, Manolis Zitnik, Marinka Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology. Image-based spatial transcriptomics technologies now provide high-resolution measurements of cell images and gene expression profiles, but existing methods typically analyze these modalities in isolation or at limited resolution. We address the problem by introducing SPATIA, a multi-level generative and predictive model that learns unified, spatially aware representations by fusing morphology, gene expression, and spatial context from the cell to the tissue level. SPATIA also incorporates a novel spatially conditioned generative framework for predicting cell morphologies under perturbations. Specifically, we propose a confidence-aware flow matching objective that reweights weak optimal-transport pairs based on uncertainty. We further apply morphology-profile alignment to encourage biologically meaningful image generation, enabling the modeling of microenvironment-dependent phenotypic transitions. We assembled a multi-scale dataset consisting of 25.9 million cell-gene pairs across 17 tissues. We benchmark SPATIA against 18 models across 12 tasks, spanning categories such as phenotype generation, annotation, clustering, gene imputation, and cross-modal prediction. SPATIA achieves improved performance over state-of-the-art models, improving generative fidelity by 8% and predictive accuracy by up to 3%. |
| title | SPATIA: Multimodal Generation and Prediction of Spatial Cell Phenotypes |
| topic | Quantitative Methods Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.04704 |