stEnTrans: Transformer-based deep learning for spatial transcriptomics enhancement

TL;DR

stEnTrans is a Transformer-based deep learning method that enhances spatial transcriptomics data by improving resolution and predicting gene expression in unmeasured areas, aiding biological insights.

Contribution

It introduces a self-supervised Transformer model for spatial transcriptomics enhancement without requiring extra data, outperforming existing methods.

Findings

Superior resolution enhancement over traditional methods

Accurate prediction of gene expression in unmeasured areas

Facilitates discovery of spatial biological patterns

Abstract

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.