{"id":"doi:10.1186/s13073-024-01283-x","kind":"article","title":"Unsupervised spatially embedded deep representation of spatial transcriptomics","authors":["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"],"author_count":14,"year":2024,"published":"2024-01-12","venue":"Genome medicine","cited_by":325,"doi":"10.1186/s13073-024-01283-x","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1186/s13073-024-01283-x","archive_url":"https://space-kitty.com/papers/doi:10.1186/s13073-024-01283-x","updated":null,"pmid":"38217035","pmcid":"PMC10790257","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC10790257/pdf","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_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"Abstract of \"Unsupervised spatially embedded deep representation of spatial transcriptomics\" by Xu H, Fu H, Long Y et al., https://doi.org/10.1186/s13073-024-01283-x, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.","topics":[{"slug":"single-cell-spatial","name":"Single-Cell & Spatial Omics","domain_slug":"biotech"}],"cited_in_dossiers":[]}