Song Tao, Dai Huanhuan, Wang Shuang, Wang Gan, Zhang Xudong, Zhang Ying, Jiao Linfang
College of Computer Science and Technology, China University of Petroleum (East China), Qingdao, China.
Department of Artificial Intelligence, Faculty of Computer Science, Campus de Montegancedo, Polytechnical University of Madrid, Boadilla Del Monte, Madrid, Spain.
Front Genet. 2022 Oct 11;13:1038919. doi: 10.3389/fgene.2022.1038919. eCollection 2022.
Recent advances in single-cell RNA sequencing (scRNA-seq) have accelerated the development of techniques to classify thousands of cells through transcriptome profiling. As more and more scRNA-seq data become available, supervised cell type classification methods using externally well-annotated source data become more popular than unsupervised clustering algorithms. However, accurate cellular annotation of single cell transcription data remains a significant challenge. Here, we propose a hybrid network structure called TransCluster, which uses linear discriminant analysis and a modified Transformer to enhance feature learning. It is a cell-type identification tool for single-cell transcriptomic maps. It shows high accuracy and robustness in many cell data sets of different human tissues. It is superior to other known methods in external test data set. To our knowledge, TransCluster is the first attempt to use Transformer for annotating cell types of scRNA-seq, which greatly improves the accuracy of cell-type identification.
单细胞RNA测序（scRNA-seq）的最新进展加速了通过转录组分析对数千个细胞进行分类的技术发展。随着越来越多的scRNA-seq数据可用，使用外部注释良好的源数据的监督细胞类型分类方法比无监督聚类算法更受欢迎。然而，单细胞转录数据的准确细胞注释仍然是一项重大挑战。在这里，我们提出了一种称为TransCluster的混合网络结构，它使用线性判别分析和改进的Transformer来增强特征学习。它是一种用于单细胞转录组图谱的细胞类型识别工具。它在许多不同人类组织的细胞数据集中显示出高精度和鲁棒性。在外部测试数据集中它优于其他已知方法。据我们所知，TransCluster是首次尝试使用Transformer对scRNA-seq的细胞类型进行注释，这大大提高了细胞类型识别的准确性。
