DeepST: identifying spatial domains in spatial transcriptomics by deep learning.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

引用：Xu C, Jin X, Wei S, Wang P, Luo M, Xu Z, Yang W, Cai Y, Xiao L, Lin X, Liu H, Cheng R, Pang F, Chen R, Su X, Hu Y, Wang G, Jiang Q. (2022). DeepST: identifying spatial domains in spatial transcriptomics by deep learning. Nucleic acids research. PMID 36250636 · PMC9825193 · DOI 10.1093/nar/gkac901

证据类型：PRIMARY_RESEARCH

主题：rna-seq、spatial-omics

被引次数（采集时）：205

原始记录：[Europe PMC](https://europepmc.org/article/MED/36250636)

来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

作者摘要（按来源许可复用）

Recent advances in spatial transcriptomics (ST) have brought unprecedented opportunities to understand tissue organization and function in spatial context. However, it is still challenging to precisely dissect spatial domains with similar gene expression and histology in situ. Here, we present DeepST, an accurate and universal deep learning framework to identify spatial domains, which performs better than the existing state-of-the-art methods on benchmarking datasets of the human dorsolateral prefrontal cortex. Further testing on a breast cancer ST dataset, we showed that DeepST can dissect spatial domains in cancer tissue at a finer scale. Moreover, DeepST can achieve not only effective batch integration of ST data generated from multiple batches or different technologies, but also expandable capabilities for processing other spatial omics data. Together, our results demonstrate that DeepST has the exceptional capacity for identifying spatial domains, making it a desirable tool to gain novel insights from ST studies.

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