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

Ma, X.; Jin, W.; Lu, Q.; sun, r. c.; Chen, L.

2025-09-17 bioinformatics
10.1101/2025.09.11.675689 bioRxiv
Show 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 includes an interactive annotation interface and a model training module for spatial domain detection, providing a unified and user-friendly framework to facilitate spatial domain analysis.

Published in Genome Research (predicted rank #6) · training set

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