Deciphering spatial domains from spatially resolved transcriptomics through spatially regularized deep graph networks.

ABSTRACT:

Background

Recent advancements in spatially resolved transcriptomics (SRT) have opened up unprecedented opportunities to explore gene expression patterns within spatial contexts. Deciphering spatial domains is a critical task in spatial transcriptomic data analysis, aiding in the elucidation of tissue structural heterogeneity and biological functions. However, existing spatial domain detection methods ignore the consistency of expression patterns and spatial arrangements between spots, as well as the severe gene dropout phenomenon present in SRT data, resulting in suboptimal performance in identifying tissue spatial heterogeneity.

Results

In this paper, we introduce a novel framework, spatially regularized deep graph networks (SR-DGN), which integrates gene expression profil

SUBMITTER: Zhang D 

PROVIDER: S-EPMC11607964 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature