Unsupervised spatially embedded deep representation of spatial transcriptomics.

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

书目信息

引用：Xu H, Fu H, Long Y, Ang KS, Sethi R, Chong K, Li M, Uddamvathanak R, Lee HK, Ling J, Chen A, Shao L, Liu L, Chen J. (2024). Unsupervised spatially embedded deep representation of spatial transcriptomics. Genome medicine. PMID 38217035 · PMC10790257 · DOI 10.1186/s13073-024-01283-x

证据类型：PRIMARY_RESEARCH

主题：rna-seq、spatial-omics

被引次数（采集时）：229

原始记录：[Europe PMC](https://europepmc.org/article/MED/38217035)

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

作者摘要（按来源许可复用）

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR's ability to impute and denoise gene expression (URL: https://github.com/JinmiaoChenLab/SEDR/ ).

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