Stem cells and the cells they produce are unique because they vary from one cell to another. Traditional methods of studying cells often overlook these differences. However, the development of new technologies for studying individual cells has greatly changed biological research in recent years. Among these innovations, single-cell RNA sequencing (scRNA-seq) stands out. This technique allows scientists to examine the activity of genes in each cell, across thousands or even millions of cells. This makes it possible to understand the diversity of cells, identify new types of cells, and see how cells differ across different tissues, individuals, species, times, and conditions. This paper discusses the importance of scRNA-seq and the computational tools and software that are essential for analyzing the vast amounts of data generated by scRNA-seq studies. Our goal is to provide practical advice for bioinformaticians and biologists who are using scRNA-seq to study stem cells. We offer an overview of the scRNA-seq field, including the tools available, how they can be used, and how to present the results of these studies effectively. Our findings include a detailed overview and classification of tools used in scRNA-seq analysis, based on a review of 2,733 scientific publications. This review is complemented by information from the scRNA-tools database, which lists over 1,400 tools for analyzing scRNA-seq data. This database is an invaluable resource for researchers, offering a wide range of options for analyzing their scRNA-seq data.

中文翻译：

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干细胞研究 scRNA 测序数据分析中选择和利用最佳特征的路线图：综合综述。

干细胞及其产生的细胞是独特的，因为它们在不同细胞之间有所不同。研究细胞的传统方法常常忽视这些差异。然而，近年来研究单个细胞的新技术的发展极大地改变了生物学研究。在这些创新中，单细胞 RNA 测序 (scRNA-seq) 脱颖而出。这项技术使科学家能够检查数千甚至数百万个细胞中每个细胞中基因的活性。这使得了解细胞的多样性、识别新的细胞类型以及了解细胞在不同组织、个体、物种、时间和条件下的差异成为可能。本文讨论了 scRNA-seq 的重要性以及分析 scRNA-seq 研究生成的大量数据所必需的计算工具和软件。我们的目标是为使用 scRNA-seq 研究干细胞的生物信息学家和生物学家提供实用建议。我们概述了 scRNA-seq 领域，包括可用的工具、如何使用它们以及如何有效地呈现这些研究的结果。我们的研究结果包括基于对 2,733 份科学出版物的回顾，对 scRNA-seq 分析中使用的工具进行了详细概述和分类。 scRNA-tools 数据库的信息补充了这篇综述，该数据库列出了 1,400 多种用于分析 scRNA-seq 数据的工具。该数据库对于研究人员来说是宝贵的资源，为分析 scRNA-seq 数据提供了广泛的选择。