Spatial domains identification based on multi-view contrastive learning in spatial transcriptomics

Spatial transcriptomic techniques can be used to obtain transcriptome data from different locations in tissues. The identification of spatial domains is a key task in the analysis of spatial transcriptomic data. Therefore, we propose a multi-view contrastive learning framework named MVCLST for identifying spatial domains. First, MVCLST introduces pathway information on the basis of spatial transcriptomic data to consider the functional correlation between spots. In order to better mine the underlying biological features, MVCLST constructs three biological networks from three biological perspectives: spatial distribution, similarity of gene expression and functional correlation. Secondly, MVCLST introduces a multi-view contrastive learning method, which fully considers the contrastive relationship between multiple views to improve the accuracy and reliability of feature extraction. Then, in order to obtain a more abundant feature representation, the adaptive attention mechanism is used to integrate the common features and specific features. Finally, we compare MVCLST with five spatial transcriptomic methods to verify the accuracy of MVCLST for spatial domains identification. The experimental results show that MVCLST is better than the other five methods.