{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T16:12:04Z","timestamp":1775664724262,"version":"3.50.1"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFF1201201"],"award-info":[{"award-number":["2021YFF1201201"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,3,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Spatial transcriptomics (ST) data have emerged as a pivotal approach to comprehending the function and interplay of cells within intricate tissues. Nonetheless, analyses of ST data are restricted by the low spatial resolution and limited number of ribonucleic acid transcripts that can be detected with several popular ST techniques. In this study, we propose that both of the above issues can be significantly improved by introducing a deep graph co-embedding framework. First, we establish a self-supervised, co-graph convolution network\u2013based deep learning model termed SpatialcoGCN, which leverages single-cell data to deconvolve the cell mixtures in spatial data. Evaluations of SpatialcoGCN on a series of simulated ST data and real ST datasets from human ductal carcinoma in situ, developing human heart and mouse brain suggest that SpatialcoGCN could outperform other state-of-the-art cell type deconvolution methods in estimating per-spot cell composition. Moreover, with competitive accuracy, SpatialcoGCN could also recover the spatial distribution of transcripts that are not detected by raw ST data. With a similar co-embedding framework, we further established a spatial information\u2013aware ST data simulation method, SpatialcoGCN-Sim. SpatialcoGCN-Sim could generate simulated ST data with high similarity to real datasets. Together, our approaches provide efficient tools for studying the spatial organization of heterogeneous cells within complex tissues.<\/jats:p>","DOI":"10.1093\/bib\/bbae130","type":"journal-article","created":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T09:52:04Z","timestamp":1711965124000},"source":"Crossref","is-referenced-by-count":14,"title":["SpatialcoGCN: deconvolution and spatial information\u2013aware simulation of spatial transcriptomics data via deep graph co-embedding"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-6250-3847","authenticated-orcid":false,"given":"Wang","family":"Yin","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University , 38 Xueyuan Road, Beijing 100191 , China"},{"name":"State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University , 38 Xueyuan Road, Beijing 100191 , China"},{"name":"Department of Neurobiology, School of Basic Medical Sciences, Neuroscience Research Institute, Peking University , 38 Xueyuan Road, Beijing 100191 , China"}]},{"given":"You","family":"Wan","sequence":"additional","affiliation":[{"name":"Department of Neurobiology, School of Basic Medical Sciences, Neuroscience Research Institute, Peking University , 38 Xueyuan Road, Beijing 100191 , China"}]},{"given":"Yuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University , 38 Xueyuan Road, Beijing 100191 , China"},{"name":"State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University , 38 Xueyuan Road, Beijing 100191 , China"}]}],"member":"286","published-online":{"date-parts":[[2024,3,31]]},"reference":[{"key":"2024040109513938600_ref1","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1038\/s41581-018-0021-7","article-title":"Single-cell RNA sequencing for the study of development, physiology and 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