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) 已成为许多研究学科科学家的强大工具，因为它能够阐明不同组织和细胞群的异质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对于手动注释来说既耗时又依赖于知识。相比之下，自动细胞类型识别方法可能具有快速、准确和更用户友好的优点。在这里，我们讨论和评估了 32 种已发表的用于 scRNA-seq 数据分析的自动方法，包括预测准确性、F1 分数、未标记率和运行时间。我们强调了这些方法的优点和缺点，并根据可用信息提供方法选择建议。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 数据分析包，其中包含所讨论的自动方法，以帮助在实际应用中轻松使用它们。