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Due to the high dimensionality and high noises in transcriptomic data, it is difficult to infer stable gene co-expression associations from single dataset. Meta-analysis of multisource data can effectively tackle this problem. We proposed Joint Embedding of multiple BIpartite Networks (JEBIN) to learn the low-dimensional consensus representation for genes by integrating multiple expression datasets. JEBIN infers gene co-expression associations in a nonlinear and global similarity manner and can integrate datasets with different distributions in linear time complexity with the gene and total sample size. The effectiveness and scalability of JEBIN were verified by simulation experiments, and its superiority over the commonly used integration methods was proved by three indexes on real biological datasets. Then, JEBIN was applied to study the gene co-expression patterns of hepatocellular carcinoma (HCC) based on multiple expression datasets of HCC and adjacent normal tissues, and further on latest HCC single-cell RNA-seq data. Results show that gene co-expressions are highly different between bulk and single-cell datasets. Finally, many differentially co-expressed ligand\u2013receptor pairs were discovered by comparing HCC with adjacent normal data, providing candidate HCC targets for abnormal cell\u2013cell communications.<\/jats:p>","DOI":"10.1093\/bib\/bbab603","type":"journal-article","created":{"date-parts":[[2021,12,28]],"date-time":"2021-12-28T07:40:30Z","timestamp":1640677230000},"source":"Crossref","is-referenced-by-count":1,"title":["JEBIN: analyzing gene co-expressions across multiple datasets by joint network embedding"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6189-4795","authenticated-orcid":false,"given":"Guiying","family":"Wu","sequence":"first","affiliation":[{"name":"MOE Key Laboratory of Bioinformatics, BNRIST Bioinformatics Division, Department of Automation, Tsinghua University, Beijing 100084, 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