{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T22:37:18Z","timestamp":1775255838852,"version":"3.50.1"},"reference-count":57,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T00:00:00Z","timestamp":1729728000000},"content-version":"vor","delay-in-days":31,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42374174"],"award-info":[{"award-number":["42374174"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The integration of single-cell RNA sequencing (scRNA-seq) data from multiple experimental batches enables more comprehensive characterizations of cell states. Given that existing methods disregard the structural information between cells and genes, we proposed a structure-preserved scRNA-seq data integration approach using heterogeneous graph neural network (scHetG). By establishing a heterogeneous graph that represents the interactions between multiple batches of cells and genes, and combining a heterogeneous graph neural network with contrastive learning, scHetG concurrently obtained cell and gene embeddings with structural information. A comprehensive assessment covering different species, tissues and scales indicated that scHetG is an efficacious method for eliminating batch effects while preserving the structural information of cells and genes, including batch-specific cell types and cell-type specific gene co-expression patterns.<\/jats:p>","DOI":"10.1093\/bib\/bbae538","type":"journal-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T14:29:43Z","timestamp":1729780183000},"source":"Crossref","is-referenced-by-count":2,"title":["Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network"],"prefix":"10.1093","volume":"25","author":[{"given":"Xun","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Mathematics and Physics, China University of Geosciences , Wuhan 430074 ,","place":["China"]}]},{"given":"Kun","family":"Qian","sequence":"additional","affiliation":[{"name":"School of Mathematics and Physics, China University of Geosciences , Wuhan 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