Reference-Informed Spatial Domain Detection Using Weak Supervision for Spatial Transcriptomics.

ABSTRACT: One of the key objectives in spatial transcriptomics (ST) studies is to map the complex organization and functions of tissues. We introduce GraphScrDom, a reference-informed and weakly supervised contrastive learning model that uniquely integrates expert-provided manual annotations (i.e., scribbles) on spatial grids or histology images with cell type-specific gene expression profiles derived from reference single-cell RNA-seq data to perform tissue segmentation. With only limited scribble annotations, GraphScrDom consistently outperforms existing methods across various ST platforms and at both bulk and single-cell resolutions, as evaluated by six widely used metrics, demonstrating strong generalizability and robustness. Additionally, we have developed an integrative software toolkit that i

SUBMITTER: Ma X 

PROVIDER: S-EPMC12458121 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature