# stEnTrans: Transformer-based deep learning for spatial transcriptomics enhancement

arXiv: https://arxiv.org/abs/2407.08224 · HTML: https://mlsanity.com/paper/2407.08224/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 11 Jul 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Self-Attention with Relative Position Representations](https://mlsanity.com/paper/1803.02155/) (2018)
- [Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/1902.06162/) (2019)
- [On the Expressivity Role of LayerNorm in Transformers' Attention](https://mlsanity.com/paper/2305.02582/) (2023)
- [Rethinking Skip Connection with Layer Normalization in Transformers and ResNets](https://mlsanity.com/paper/2105.07205/) (2021)
- [stMCDI: Masked Conditional Diffusion Model with Graph Neural Network for Spatial Transcriptomics Data Imputation](https://mlsanity.com/paper/2403.10863/) (2024)

## Built on by (most cited first, 3 of 3)

- [SpaDiT: Diffusion Transformer for Spatial Gene Expression Prediction using scRNA-seq](https://mlsanity.com/paper/2407.13182/) (2024)
- [High-Resolution Spatial Transcriptomics from Histology Images using HisToSGE](https://mlsanity.com/paper/2407.20518/) (2024)
- [SpaCRD: Multimodal Deep Fusion of Histology and Spatial Transcriptomics for Cancer Region Detection](https://mlsanity.com/paper/2603.06186/) (2026)

## Abstract (arXiv)

The spatial location of cells within tissues and organs is crucial for the manifestation of their specific functions.Spatial transcriptomics technology enables comprehensive measurement of the gene expression patterns in tissues while retaining spatial information. However, current popular spatial transcriptomics techniques either have shallow sequencing depth or low resolution. We present stEnTrans, a deep learning method based on Transformer architecture that provides comprehensive predictions for gene expression in unmeasured areas or unexpectedly lost areas and enhances gene expression in original and inputed spots. Utilizing a self-supervised learning approach, stEnTrans establishes proxy tasks on gene expression profile without requiring additional data, mining intrinsic features of the tissues as supervisory information. We evaluate stEnTrans on six datasets and the results indicate superior performance in enhancing spots resolution and predicting gene expression in unmeasured areas compared to other deep learning and traditional interpolation methods. Additionally, Our method also can help the discovery of spatial patterns in Spatial Transcriptomics and enrich to more biologically significant pathways. Our source code is available at https://github.com/shuailinxue/stEnTrans.