Unsupervised spatially embedded deep representation of spatial transcriptomics
-
Published:2024-01-12
Issue:1
Volume:16
Page:
-
ISSN:1756-994X
-
Container-title:Genome Medicine
-
language:en
-
Short-container-title:Genome Med
Author:
Xu Hang, Fu Huazhu, Long Yahui, Ang Kok Siong, Sethi Raman, Chong Kelvin, Li Mengwei, Uddamvathanak Rom, Lee Hong Kai, Ling Jingjing, Chen Ao, Shao Ling, Liu Longqi, Chen JinmiaoORCID
Abstract
AbstractOptimal 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/).
Funder
National Research Foundation Singapore A Star, Singapore A Star Singapore
Publisher
Springer Science and Business Media LLC
Subject
Genetics (clinical),Genetics,Molecular Biology,Molecular Medicine
Reference60 articles.
1. Stahl PL, Salmen F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, Giacomello S, Asp M, Westholm JO, Huss M, et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science. 2016;353:78–82. 2. Maynard KR, Collado-Torres L, Weber LM, Uytingco C, Barry BK, Williams SR, Catallini JL 2nd, Tran MN, Besich Z, Tippani M, et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat Neurosci. 2021;24:425–36 (https://research.libd.org/globus/jhpce_HumanPilot410x/index.html). 3. Ji AL, Rubin AJ, Thrane K, Jiang S, Reynolds DL, Meyers RM, Guo MG, George BM, Mollbrink A, Bergenstrahle J, et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell. 2020;182(497–514):e422. 4. Rodriques SG, Stickels RR, Goeva A, Martin CA, Murray E, Vanderburg CR, Welch J, Chen LM, Chen F, Macosko EZ. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science. 2019;363:1463–7. 5. Chen A, Liao S, Cheng M, Ma K, Wu L, Lai Y, Qiu X, Yang J, Xu J, Hao S, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022;185(1777–1792):e1721.
|
|