Prioritizing perturbation-responsive gene patterns using interpretable deep learning
Abstract Spatially resolved transcriptomics enables mapping of multiplexed gene expression within tissue contexts. While existing methods prioritize spatially variable genes within a single slice, few address identifying genes with differential spatial expression patterns (DSEPs) across multiple con...
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61476-9 |
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| author | Yan Cui Zhiyuan Yuan |
| author_facet | Yan Cui Zhiyuan Yuan |
| author_sort | Yan Cui |
| collection | DOAJ |
| description | Abstract Spatially resolved transcriptomics enables mapping of multiplexed gene expression within tissue contexts. While existing methods prioritize spatially variable genes within a single slice, few address identifying genes with differential spatial expression patterns (DSEPs) across multiple conditions—an critical need for complex experimental designs. Challenges include modeling cross-slice spatial variation, scalability to large datasets, and disentangling inter-slice heterogeneity. We introduce DSEP gene prioritization as a new analytical task and present River, an interpretable deep learning framework that identifies genes exhibiting condition-relevant spatial changes. River features a two-branch predictive architecture and a post hoc attribution strategy to rank genes (or other features) by their contribution to condition differences. Its spatially-informed modeling ensures scalability to large spatial datasets, and we further decouple spatial and non-spatial components to enhance interpretability. We evaluate River on simulations and apply it to diverse biological contexts, including embryogenesis, diabetes-affected spermatogenesis, and lupus-associated splenic changes. In triple-negative breast cancer, River prioritizes survival-associated spatial patterns that generalize across patients. River is distribution-agnostic and compatible with diverse spatial data types, offering a flexible and scalable solution for analyzing tissue-wide expression dynamics across multiple biological conditions. |
| format | Article |
| id | doaj-art-e449de4897c74ad9847b74cfcc05663c |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-e449de4897c74ad9847b74cfcc05663c2025-08-20T03:37:37ZengNature PortfolioNature Communications2041-17232025-07-0116111910.1038/s41467-025-61476-9Prioritizing perturbation-responsive gene patterns using interpretable deep learningYan Cui0Zhiyuan Yuan1Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan UniversityCenter for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan UniversityAbstract Spatially resolved transcriptomics enables mapping of multiplexed gene expression within tissue contexts. While existing methods prioritize spatially variable genes within a single slice, few address identifying genes with differential spatial expression patterns (DSEPs) across multiple conditions—an critical need for complex experimental designs. Challenges include modeling cross-slice spatial variation, scalability to large datasets, and disentangling inter-slice heterogeneity. We introduce DSEP gene prioritization as a new analytical task and present River, an interpretable deep learning framework that identifies genes exhibiting condition-relevant spatial changes. River features a two-branch predictive architecture and a post hoc attribution strategy to rank genes (or other features) by their contribution to condition differences. Its spatially-informed modeling ensures scalability to large spatial datasets, and we further decouple spatial and non-spatial components to enhance interpretability. We evaluate River on simulations and apply it to diverse biological contexts, including embryogenesis, diabetes-affected spermatogenesis, and lupus-associated splenic changes. In triple-negative breast cancer, River prioritizes survival-associated spatial patterns that generalize across patients. River is distribution-agnostic and compatible with diverse spatial data types, offering a flexible and scalable solution for analyzing tissue-wide expression dynamics across multiple biological conditions.https://doi.org/10.1038/s41467-025-61476-9 |
| spellingShingle | Yan Cui Zhiyuan Yuan Prioritizing perturbation-responsive gene patterns using interpretable deep learning Nature Communications |
| title | Prioritizing perturbation-responsive gene patterns using interpretable deep learning |
| title_full | Prioritizing perturbation-responsive gene patterns using interpretable deep learning |
| title_fullStr | Prioritizing perturbation-responsive gene patterns using interpretable deep learning |
| title_full_unstemmed | Prioritizing perturbation-responsive gene patterns using interpretable deep learning |
| title_short | Prioritizing perturbation-responsive gene patterns using interpretable deep learning |
| title_sort | prioritizing perturbation responsive gene patterns using interpretable deep learning |
| url | https://doi.org/10.1038/s41467-025-61476-9 |
| work_keys_str_mv | AT yancui prioritizingperturbationresponsivegenepatternsusinginterpretabledeeplearning AT zhiyuanyuan prioritizingperturbationresponsivegenepatternsusinginterpretabledeeplearning |