stMHCG: High-confidence multi-view clustering for identification of spatial domains from spatially resolved transcriptomics

Abstract

Recent advancements within the field of spatially resolved transcriptomics (SRT) have vastly augmented the repertoire of opportunities for delineating the intricate landscapes of gene expression across tissue spatial dimensions. Currently proposed methods primarily focus on examining the consistency information between spatial location and gene expression combinations, yet they overlook potential complementary information. In response to this, we propose a multi-view clustering method for SRT data, named stMHCG, which is designed to address the challenge of inadequate spatial expression information learning. Firstly, by utilizing a spatial expression augmentation module, we conduct an in-depth exploration of the transcriptome signal differences between individual spots and their physically adjacent spots, capturing subtle variations that may be overlooked in traditional spatial view constructions and generating enriched, complementary augmented views. Furthermore, to enhance the guiding role of consistent information in the clustering process, we have designed a high-confidence clustering guidance module. This module dynamically calculates the target distribution for each view and adjusts the clustering results accordingly, thereby enabling a finer-grained segmentation of the spatial domain. We validated stMHCG across multiple tissue types and technology platforms by comparing it with existing typical methods. Experimental results demonstrate that stMHCG exhibits excellent performance in downstream analysis tasks such as spatial domain identification, trajectory inference, and data denoising of SRT data.