Li Tianhao, Wang Zixuan, Liu Yuhang, He Sihan, Zou Quan, Zhang Yongqing
School of Computer Science, Chengdu University of Information Technology, No. 24 Block 1, Xuefu Road, 610225 Chengdu, China.
College of Electronics and Information Engineering, Sichuan University, No. 24 South Section 1, 1st Ring Road, 610065 Chengdu, China.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf207.
The rapid accumulation of single-cell RNA sequencing data has provided unprecedented computational resources for cell type annotation, significantly advancing our understanding of cellular heterogeneity. Leveraging gene expression profiles derived from transcriptomic data, researchers can accurately infer cell types, sparking the development of numerous innovative annotation methods. These methods utilize a range of strategies, including marker genes, correlation-based matching, and supervised learning, to classify cell types. In this review, we systematically examine these annotation approaches based on transcriptomics-specific gene expression profiles and provide a comprehensive comparison and categorization of these methods. Furthermore, we focus on the main challenges in the annotation process, especially the long-tail distribution problem arising from data imbalance in rare cell types. We discuss the potential of deep learning techniques to address these issues and enhance model capability in recognizing novel cell types within an open-world framework.
单细胞RNA测序数据的快速积累为细胞类型注释提供了前所未有的计算资源，极大地推进了我们对细胞异质性的理解。利用从转录组数据中获得的基因表达谱，研究人员可以准确推断细胞类型，从而催生了众多创新的注释方法。这些方法采用了一系列策略，包括标记基因、基于相关性的匹配和监督学习，来对细胞类型进行分类。在本综述中，我们基于转录组学特定的基因表达谱系统地研究了这些注释方法，并对这些方法进行了全面的比较和分类。此外，我们关注注释过程中的主要挑战，特别是稀有细胞类型数据不平衡所产生的长尾分布问题。我们讨论了深度学习技术解决这些问题以及在开放世界框架中增强识别新细胞类型模型能力的潜力。
