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To solve this issue, many supervised approaches have been developed to identify cell types based on the rapid accumulation of public datasets. However, these approaches are sensitive to batch effects or biological variations since the data distributions are different in cross-platforms or species predictions. In this study, we developed scAdapt, a virtual adversarial domain adaptation network, to transfer cell labels between datasets with batch effects. scAdapt used both the labeled source and unlabeled target data to train an enhanced classifier and aligned the labeled source centroids and pseudo-labeled target centroids to generate a joint embedding. The scAdapt was demonstrated to outperform existing methods for classification in simulated, cross-platforms, cross-species, spatial transcriptomic and COVID-19 immune datasets. Further quantitative evaluations and visualizations for the aligned embeddings confirm the superiority in cell mixing and the ability to preserve discriminative cluster structure present in the original datasets.<\/jats:p>","DOI":"10.1093\/bib\/bbab281","type":"journal-article","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T20:16:47Z","timestamp":1625257007000},"source":"Crossref","is-referenced-by-count":26,"title":["scAdapt: virtual adversarial domain adaptation network for single cell RNA-seq data classification across platforms and species"],"prefix":"10.1093","volume":"22","author":[{"given":"Xiang","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering at the Sun Yat-sen University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Chai","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering at the Sun Yat-sen University, 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