Deep clustering representation of spatially resolved transcriptomics data using multi-view variational graph auto-encoders with consensus clustering.

ABSTRACT: The rapid development of spatial transcriptomics (ST) technology has provided unprecedented opportunities to understand tissue relationships and functions within specific spatial contexts. Accurate identification of spatial domains is crucial for downstream spatial transcriptomics analysis. However, effectively combining gene expression data, histological images and spatial coordinate data to identify spatial domains remains a challenge. To this end, we propose STMVGAE, a novel spatial transcriptomics analysis tool that combines a multi-view variational graph autoencoder with a consensus clustering framework. STMVGAE begins by extracting histological images features using a pre-trained convolutional neural network (CNN) and integrates these features with gene expression data to generate au

SUBMITTER: Niu J 

PROVIDER: S-EPMC11664090 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature