  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.
