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Accurately determining the cell type of each cell is a crucial goal in single-cell RNA-seq (scRNA-seq) analysis. Apart from overcoming the batch effects arising from various factors, single-cell annotation methods also face the challenge of effectively processing large-scale datasets. With the availability of an increase in the scRNA-seq datasets, integrating multiple datasets and addressing batch effects originating from diverse sources are also challenges in cell-type annotation. In this work, to overcome the challenges, we developed a supervised method called CIForm based on the Transformer for cell-type annotation of large-scale scRNA-seq data. To assess the effectiveness and robustness of CIForm, we have compared it with some leading tools on benchmark datasets. Through the systematic comparisons under various cell-type annotation scenarios, we exhibit that the effectiveness of CIForm is particularly pronounced in cell-type annotation. The source code and data are available at https:\/\/github.com\/zhanglab-wbgcas\/CIForm.<\/jats:p>","DOI":"10.1093\/bib\/bbad195","type":"journal-article","created":{"date-parts":[[2023,5,18]],"date-time":"2023-05-18T16:16:00Z","timestamp":1684426560000},"source":"Crossref","is-referenced-by-count":57,"title":["CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data"],"prefix":"10.1093","volume":"24","author":[{"given":"Jing","family":"Xu","sequence":"first","affiliation":[{"name":"Wuhan Botanical Garden, Chinese Academy of Sciences Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, , Wuhan 430074 , China"},{"name":"University of Chinese Academy of Sciences , Beijing 100049 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aidi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Wuhan Botanical Garden, Chinese Academy of Sciences Key Laboratory of Plant Germplasm Enhancement and 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