CausalGeD: Blending Causality and Diffusion for Spatial Gene Expression Generation

TL;DR

CausalGeD is a novel model that integrates causality and diffusion processes to improve the generation of spatial gene expression data, capturing regulatory mechanisms without predefined relationships.

Contribution

It generalizes the Causal Attention Transformer to gene expression data, enabling better modeling of causal relationships in spatial transcriptomics.

Findings

Outperformed state-of-the-art methods by 5-32% in key metrics

Achieved higher Pearson's correlation and structural similarity

Validated across 10 tissue datasets

Abstract

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.