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GRAS4T accurately identified spatial domains in the HER2+ dataset. (a) Boxplot of ARI values across all sections of the HER2+ dataset for all compared methods. (b) The H&E image and manual annotation for the A1 section. (c) Spatial domains of HER2+ dataset in (b) detected by STAGATE, CCST, and GRAS4T.

Journal: Computational and Structural Biotechnology Journal

Article Title: Spatial domains identification in spatial transcriptomics using modality-aware and subspace-enhanced graph contrastive learning

doi: 10.1016/j.csbj.2024.10.029

Figure Lengend Snippet: GRAS4T accurately identified spatial domains in the HER2+ dataset. (a) Boxplot of ARI values across all sections of the HER2+ dataset for all compared methods. (b) The H&E image and manual annotation for the A1 section. (c) Spatial domains of HER2+ dataset in (b) detected by STAGATE, CCST, and GRAS4T.

Article Snippet: The efficacy of GRAS4T was further evaluated using the human HER2-positive breast tumor (HER2+) dataset , which used different spatial technologies (spatial transcriptomics) compared to the DLPFC dataset.

Techniques: