SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq

TL;DR

SpaDiT introduces a diffusion Transformer model that enhances spatial gene expression prediction by integrating scRNA-seq and spatial transcriptomics data, outperforming existing methods in accuracy and gene detection.

Contribution

The paper presents SpaDiT, a novel deep learning framework using diffusion Transformers to improve gene prediction and spatial structure generation in spatial transcriptomics.

Findings

SpaDiT outperforms eight baseline methods in multiple metrics.

It accurately predicts undetected genes in spatial transcriptomics data.

SpaDiT effectively generates spatial gene expression structures.

Abstract

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.