CausalGeD: Blending Causality and Diffusion for Spatial Gene Expression Generation
This work addresses a domain-specific problem for researchers in genomics and spatial biology, offering incremental improvements over existing methods.
The paper tackled the problem of integrating single-cell RNA sequencing and spatial transcriptomics data, which had limited performance with structural similarity often below 60%, by developing CausalGeD to leverage causal relationships between genes, resulting in outperforming state-of-the-art baselines by 5-32% in key metrics across 10 tissue datasets.
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.