Senlin Lin, Yan Cui, Fangyuan Zhao, Zhidong Yang, Jiangning Song, Jianhua Yao, Yu Zhao, Bin-Zhi Qian, Yi Zhao, Zhiyuan Yuan
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引用次数: 0
Abstract
Spatially resolved transcriptomics (SRT) technologies have revolutionized the study of tissue organization. We introduce a graph convolutional network with an attention and positive emphasis mechanism, termed BINARY, relying exclusively on binarized SRT data to accurately delineate spatial domains. BINARY outperforms existing methods across various SRT data types while using significantly less input information. Our study suggests that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.
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