Year: 2025
Type: conference-paper
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.
Authors: Rabeya Tus Sadia, Md Atik Ahamed, Qiang Cheng
Institution: University of Kentucky
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Awards: G2837817250, EAGER: PBI: Modeling Place-based Innovation by Leveraging AI-Enabled Dynamic Graph Techniques, Gene Profile Analysis of Associations between Alzheimer’s Disease and Circadian Rhythmic Patterns of Human Brain Regions Using Machine Learning, G6723656122, RI: Small: Optimal Transport Generative Adversarial Networks: Theory, Algorithms, and Applications, G9000011571
OpenAlex ID: W4407425874