Accurately deciphering spatial domains for spatially resolved transcriptomics with stCluster.

ABSTRACT: Spatial transcriptomics provides valuable insights into gene expression within the native tissue context, effectively merging molecular data with spatial information to uncover intricate cellular relationships and tissue organizations. In this context, deciphering cellular spatial domains becomes essential for revealing complex cellular dynamics and tissue structures. However, current methods encounter challenges in seamlessly integrating gene expression data with spatial information, resulting in less informative representations of spots and suboptimal accuracy in spatial domain identification. We introduce stCluster, a novel method that integrates graph contrastive learning with multi-task learning to refine informative representations for spatial transcriptomic data, consequently improv

SUBMITTER: Wang T 

PROVIDER: S-EPMC11771244 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature