Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866916386418196480 |
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| author | Megas, Stathis Chen, Daniel G. Polanski, Krzysztof Eliasof, Moshe Schonlieb, Carola-Bibiane Teichmann, Sarah A. |
| author_facet | Megas, Stathis Chen, Daniel G. Polanski, Krzysztof Eliasof, Moshe Schonlieb, Carola-Bibiane Teichmann, Sarah A. |
| contents | Celcomen leverages a mathematical causality framework to disentangle intra- and inter- cellular gene regulation programs in spatial transcriptomics and single-cell data through a generative graph neural network. It can learn gene-gene interactions, as well as generate post-perturbation counterfactual spatial transcriptomics, thereby offering access to experimentally inaccessible samples. We validated its disentanglement, identifiability, and counterfactual prediction capabilities through simulations and in clinically relevant human glioblastoma, human fetal spleen, and mouse lung cancer samples. Celcomen provides the means to model disease and therapy induced changes allowing for new insights into single-cell spatially resolved tissue responses relevant to human health. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_05804 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling Megas, Stathis Chen, Daniel G. Polanski, Krzysztof Eliasof, Moshe Schonlieb, Carola-Bibiane Teichmann, Sarah A. Machine Learning Tissues and Organs Celcomen leverages a mathematical causality framework to disentangle intra- and inter- cellular gene regulation programs in spatial transcriptomics and single-cell data through a generative graph neural network. It can learn gene-gene interactions, as well as generate post-perturbation counterfactual spatial transcriptomics, thereby offering access to experimentally inaccessible samples. We validated its disentanglement, identifiability, and counterfactual prediction capabilities through simulations and in clinically relevant human glioblastoma, human fetal spleen, and mouse lung cancer samples. Celcomen provides the means to model disease and therapy induced changes allowing for new insights into single-cell spatially resolved tissue responses relevant to human health. |
| title | Celcomen: spatial causal disentanglement for single-cell and tissue perturbation modeling |
| topic | Machine Learning Tissues and Organs |
| url | https://arxiv.org/abs/2409.05804 |