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However, there is still lack of tools that could integrate these unlimited accumulations of single-cell expression data. Here, we presented a universal approach iSEEEK for integrating super large-scale single-cell expression via exploring expression rankings of top-expressing genes. We developed iSEEEK with 11.9 million single cells. We demonstrated the efficiency of iSEEEK with canonical single-cell downstream tasks on five heterogenous datasets encompassing human and mouse samples. iSEEEK achieved good clustering performance benchmarked against well-annotated cell labels. In addition, iSEEEK could transfer its knowledge learned from large-scale expression data on new dataset that was not involved in its development. iSEEEK enables identification of gene\u2013gene interaction networks that are characteristic of specific cell types. Our study presents a simple and yet effective method to integrate super large-scale single-cell transcriptomes and would facilitate translational single-cell research from bench to bedside.<\/jats:p>","DOI":"10.1093\/bib\/bbab573","type":"journal-article","created":{"date-parts":[[2021,12,14]],"date-time":"2021-12-14T15:13:41Z","timestamp":1639494821000},"source":"Crossref","is-referenced-by-count":12,"title":["A universal approach for integrating super large-scale single-cell transcriptomes by exploring gene rankings"],"prefix":"10.1093","volume":"23","author":[{"given":"Hongru","family":"Shen","sequence":"first","affiliation":[{"name":"Tianjin Cancer Institute, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, 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