StMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multimodal feature representation.

ABSTRACT:

Background

Deciphering spatial domains using spatially resolved transcriptomics (SRT) is of great value for characterizing and understanding tissue architecture. However, the inherent heterogeneity and varying spatial resolutions present challenges in the joint analysis of multimodal SRT data.

Results

We introduce a multimodal geometric deep learning method, named stMMR, to effectively integrate gene expression, spatial location, and histological information for accurate identifying spatial domains from SRT data. stMMR uses graph convolutional networks and a self-attention module for deep embedding of features within unimodality and incorporates similarity contrastive learning for integrating features across modalities.

Conclusions

Comprehensive benchmark analysis on

SUBMITTER: Zhang D 

PROVIDER: S-EPMC11604062 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature