SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq
This addresses the problem of incomplete gene detection in spatial transcriptomics for bioinformatics researchers, representing a novel method for a known bottleneck.
The paper tackled the limitation of spatial transcriptomics methods in cellular resolution and gene detection by proposing SpaDiT, a diffusion Transformer model that integrates scRNA-seq and ST data to predict undetected genes, achieving state-of-the-art performance compared to eight baseline methods.
The rapid development of spatial transcriptomics (ST) technologies is revolutionizing our understanding of the spatial organization of biological tissues. Current ST methods, categorized into next-generation sequencing-based (seq-based) and fluorescence in situ hybridization-based (image-based) methods, offer innovative insights into the functional dynamics of biological tissues. However, these methods are limited by their cellular resolution and the quantity of genes they can detect. To address these limitations, we propose SpaDiT, a deep learning method that utilizes a diffusion generative model to integrate scRNA-seq and ST data for the prediction of undetected genes. By employing a Transformer-based diffusion model, SpaDiT not only accurately predicts unknown genes but also effectively generates the spatial structure of ST genes. We have demonstrated the effectiveness of SpaDiT through extensive experiments on both seq-based and image-based ST data. SpaDiT significantly contributes to ST gene prediction methods with its innovative approach. Compared to eight leading baseline methods, SpaDiT achieved state-of-the-art performance across multiple metrics, highlighting its substantial bioinformatics contribution.