School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China.
Brief Bioinform. 2024 Jul 25;25(5). doi: 10.1093/bib/bbae370.
Recent advances in single-cell technologies enable the rapid growth of multi-omics data. Cell type annotation is one common task in analyzing single-cell data. It is a challenge that some cell types in the testing set are not present in the training set (i.e. unknown cell types). Most scATAC-seq cell type annotation methods generally assign each cell in the testing set to one known type in the training set but neglect unknown cell types. Here, we present OVAAnno, an automatic cell types annotation method which utilizes open-set domain adaptation to detect unknown cell types in scATAC-seq data. Comprehensive experiments show that OVAAnno successfully identifies known and unknown cell types. Further experiments demonstrate that OVAAnno also performs well on scRNA-seq data. Our codes are available online at https://github.com/lisaber/OVAAnno/tree/master.
单细胞技术的最新进展使得多组学数据迅速增长。细胞类型注释是分析单细胞数据的常见任务之一。一个挑战是测试集中的一些细胞类型不存在于训练集中（即未知细胞类型）。大多数 scATAC-seq 细胞类型注释方法通常将测试集中的每个细胞分配给训练集中的一个已知类型，但忽略未知细胞类型。在这里，我们提出了 OVAAnno，这是一种利用开放集领域自适应来检测 scATAC-seq 数据中未知细胞类型的自动细胞类型注释方法。综合实验表明，OVAAnno 成功地识别了已知和未知的细胞类型。进一步的实验表明，OVAAnno 在 scRNA-seq 数据上也表现良好。我们的代码可在 https://github.com/lisaber/OVAAnno/tree/master 上获得。
