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

arXiv: https://arxiv.org/abs/2407.13182 · HTML: https://mlsanity.com/paper/2407.13182/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models.
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Placed in

- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 7 of 7)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Equivariant 3D-Conditional Diffusion Models for Molecular Linker Design](https://mlsanity.com/paper/2210.05274/) (2022)
- [stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation](https://mlsanity.com/paper/2403.10863/) (2024)
- [stEnTrans: Transformer-based deep learning for spatial transcriptomics enhancement](https://mlsanity.com/paper/2407.08224/) (2024)

## Built on by (most cited first, 6 of 6)

- [High-Resolution Spatial Transcriptomics from Histology Images using HisToSGE](https://mlsanity.com/paper/2407.20518/) (2024)
- [Masked Graph Autoencoders with Contrastive Augmentation for Spatially Resolved Transcriptomics Data](https://mlsanity.com/paper/2408.06377/) (2024)
- [Multi-Slice Spatial Transcriptomics Data Integration Analysis with STG3Net](https://mlsanity.com/paper/2408.15246/) (2024)
- [Masked adversarial neural network for cell type deconvolution in spatial transcriptomics](https://mlsanity.com/paper/2408.05065/) (2024)
- [CausalGeD: Blending Causality and Diffusion for Spatial Gene Expression Generation](https://mlsanity.com/paper/2502.07751/) (2025)
- [FLAG: Foundation model representation with Latent diffusion Alignment via Graph for spatial gene expression prediction](https://mlsanity.com/paper/2605.18055/) (2026)

## Abstract (arXiv)

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.