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Unsupervised spatially embedded deep representation of spatial transcriptomics.

ABSTRACT: 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/ ).

SUBMITTER: Xu H

PROVIDER: S-EPMC10790257 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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Unsupervised spatially embedded deep representation of spatial transcriptomics.

Xu Hang H Fu Huazhu H Long Yahui Y Ang Kok Siong KS Sethi Raman R Chong Kelvin K Li Mengwei M Uddamvathanak Rom R Lee Hong Kai HK Ling Jingjing J Chen Ao A Shao Ling L Liu Longqi L Chen Jinmiao J

Genome medicine 20240112 1

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 grap ...[more]

PMID: 38217035

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