Complete spatially resolved gene expression is not necessary for identifying spatial domains.

IF 11.1 Q1 CELL BIOLOGY Cell genomics Pub Date : 2024-06-12 Epub Date: 2024-05-22 DOI:10.1016/j.xgen.2024.100565
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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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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完整的空间解析基因表达并不是识别空间域的必要条件。
空间分辨转录组学(SRT)技术彻底改变了对组织结构的研究。我们介绍了一种具有注意力和正向强调机制的图卷积网络,称为 "BINARY",它完全依靠二值化的 SRT 数据来精确划分空间域。在各种 SRT 数据类型中,BINARY 的表现优于现有的方法,同时使用的输入信息也少得多。我们的研究表明,精确的基因表达量化并不总是必要的,这激励我们进一步探索空间分辨二值化基因表达数据的更广泛应用。
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