# Unsupervised spatially embedded deep representation of spatial transcriptomics

Article · 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

- Archive id: doi:10.1186/s13073-024-01283-x
- Published: 2024-01-12
- Venue: Genome medicine
- DOI: https://doi.org/10.1186/s13073-024-01283-x
- PMID: 38217035
- PMCID: PMC10790257
- Citations: 325
- Source URL: https://doi.org/10.1186/s13073-024-01283-x
- PDF: https://europepmc.org/articles/PMC10790257/pdf
- Provenance: europepmc

## 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/ ).

_Abstract reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [Single-Cell & Spatial Omics](https://space-kitty.com/topics/single-cell-spatial)

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