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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2502.07751 |
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| _version_ | 1866910822280724480 |
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| author | Sadia, Rabeya Tus Ahamed, Md Atik Cheng, Qiang |
| author_facet | Sadia, Rabeya Tus Ahamed, Md Atik Cheng, Qiang |
| contents | The integration of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data is crucial for understanding gene expression in spatial context. Existing methods for such integration have limited performance, with structural similarity often below 60\%, We attribute this limitation to the failure to consider causal relationships between genes. We present CausalGeD, which combines diffusion and autoregressive processes to leverage these relationships. By generalizing the Causal Attention Transformer from image generation to gene expression data, our model captures regulatory mechanisms without predefined relationships. Across 10 tissue datasets, CausalGeD outperformed state-of-the-art baselines by 5- 32\% in key metrics, including Pearson's correlation and structural similarity, advancing both technical and biological insights. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07751 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CausalGeD: Blending Causality and Diffusion for Spatial Gene Expression Generation Sadia, Rabeya Tus Ahamed, Md Atik Cheng, Qiang Computer Vision and Pattern Recognition Genomics The integration of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data is crucial for understanding gene expression in spatial context. Existing methods for such integration have limited performance, with structural similarity often below 60\%, We attribute this limitation to the failure to consider causal relationships between genes. We present CausalGeD, which combines diffusion and autoregressive processes to leverage these relationships. By generalizing the Causal Attention Transformer from image generation to gene expression data, our model captures regulatory mechanisms without predefined relationships. Across 10 tissue datasets, CausalGeD outperformed state-of-the-art baselines by 5- 32\% in key metrics, including Pearson's correlation and structural similarity, advancing both technical and biological insights. |
| title | CausalGeD: Blending Causality and Diffusion for Spatial Gene Expression Generation |
| topic | Computer Vision and Pattern Recognition Genomics |
| url | https://arxiv.org/abs/2502.07751 |