用因果关系指导扩散模型，生成更符合真实基因表达的空间分布。
CausalGeD: Blending Causality and Diffusion for Spatial Gene Expression Generation
- 融合因果注意力与扩散机制，自动学习基因间调控关系。
- 在10个组织数据集上，结构相似度最高提升32%，相关性显著提高。
- 适合研究空间转录组与基因调控网络的生物信息学者。
单细胞RNA测序（scRNA-seq）与空间转录组学（ST）数据的整合对理解基因表达的空间特征至关重要。现有方法性能有限，结构相似度常低于60%。我们归因于未能考虑基因间的因果关系。提出CausalGeD，结合扩散与自回归过程以利用这些关系。通过将因果注意力变换器从图像生成推广至基因表达数据，模型无需预设关系即可捕捉调控机制。在10个组织数据集上，CausalGeD在皮尔逊相关性和结构相似性等关键指标上优于最先进基线5%至32%，推动了技术与生物学洞察的进步。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。