Xie Bingbing, Jiang Qin, Mora Antonio, Li Xuri
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou 510060, Guangdong, China.
Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China.
Comput Struct Biotechnol J. 2021 Oct 20;19:5874-5887. doi: 10.1016/j.csbj.2021.10.027. eCollection 2021.
Single-cell RNA sequencing (scRNA-seq) has become a powerful tool for scientists of many research disciplines due to its ability to elucidate the heterogeneous and complex cell-type compositions of different tissues and cell populations. Traditional cell-type identification methods for scRNA-seq data analysis are time-consuming and knowledge-dependent for manual annotation. By contrast, automatic cell-type identification methods may have the advantages of being fast, accurate, and more user friendly. Here, we discuss and evaluate thirty-two published automatic methods for scRNA-seq data analysis in terms of their prediction accuracy, F1-score, unlabeling rate and running time. We highlight the advantages and disadvantages of these methods and provide recommendations of method choice depending on the available information. The challenges and future applications of these automatic methods are further discussed. In addition, we provide a free scRNA-seq data analysis package encompassing the discussed automatic methods to help the easy usage of them in real-world applications.
单细胞RNA测序（scRNA-seq）已成为许多研究领域科学家的强大工具，因为它能够阐明不同组织和细胞群体的异质性和复杂细胞类型组成。用于scRNA-seq数据分析的传统细胞类型识别方法既耗时，又依赖知识进行人工注释。相比之下，自动细胞类型识别方法可能具有快速、准确且更用户友好的优点。在这里，我们根据预测准确性、F1分数、未标记率和运行时间，对32种已发表的用于scRNA-seq数据分析的自动方法进行了讨论和评估。我们强调了这些方法的优缺点，并根据可用信息提供了方法选择建议。还进一步讨论了这些自动方法的挑战和未来应用。此外，我们提供了一个免费的scRNA-seq数据分析软件包，其中包含所讨论的自动方法，以帮助它们在实际应用中易于使用。
