Spatial Gene Expression Prediction Using Multi-Neighborhood Network with Reconstructing Attention

Spatial transcriptomics (ST) has made it possible to link local spatial gene expression with the properties of tissue, which is very helpful to the research of histopathology and pathology. To obtain more ST data, we utilize deep learning methods to predict gene expression on tissue slide images. Considering the importance of the dependence of local tissue images on their neighborhoods, we propose the novel Multi-Neighborhood Network (MNN), composed of down-sampling module and vanilla Transformer blocks. Moreover, to satisfy the needs of architecture and address the computational and parameter challenges arising from it, we introduce dual-scale attention block and reconstructing attention block. To demonstrate the effectiveness of this network structure and the superiority of attention mechanisms, we conducted comparative experiments, where MNN achieved optimal PCC@M \((1\times 10^1)\) of 9.23 and 8.54 for the lung cancer and mouse brain datasets of 10x Genomics website, respectively, outperforming several state-of-the-art (SOTA) methods. This reveals the superiority of our method in terms of spatial gene prediction.