Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
FEBS J. 2024 Jun;291(12):2545-2561. doi: 10.1111/febs.17036. Epub 2024 Jan 20.
Single-cell RNA sequencing (scRNA-seq) is a technique that has proven to be a powerful tool for a wide range of fields and research studies. However, scRNA-seq data analysis has been dominated by scientists highly trained in bioinformatics or those with extensive computational experience and understanding. Recently, this trend has begun to shift as more user-friendly web-based scRNA-seq analysis tools have been developed that require little computational experience to use. However, barriers persist for nonbioinformaticians in using this technique. Complex, unfamiliar language and scarce comprehensive literature guidance to provide a framework for understanding scRNA-seq analysis outputs are among the obstacles. This work introduces many popular web-based tools for scRNA-seq and provides a general overview of their user interfaces and features. Then, a comprehensive start-to-finish introductory scRNA-seq analysis pipeline is described in detail, which aims to enable researchers to carry out scRNA-seq analysis, regardless of computational experience. Companion video tutorials can be found at "EasyScRNAseqTutorials" on YouTube (https://www.youtube.com/@scrnaseqtutorials). However, as scRNA-seq continues to penetrate new fields and expand in importance, there remains a need for more literature to help overcome barriers to its use by explaining further the highly complex and advanced analyses that are introduced within this paper.
单细胞 RNA 测序 (scRNA-seq) 已被证明是一种广泛应用于各个领域和研究的强大工具。然而，scRNA-seq 数据分析主要由在生物信息学方面受过高度训练的科学家或具有丰富计算经验和理解能力的人员主导。最近，随着更多用户友好的基于网络的 scRNA-seq 分析工具的开发，这种趋势开始发生变化，这些工具只需很少的计算经验即可使用。然而，非生物信息学家在使用该技术时仍然存在障碍。复杂、陌生的语言以及缺乏全面的文献指南来为理解 scRNA-seq 分析输出提供框架是其中的一些障碍。本工作介绍了许多流行的基于网络的 scRNA-seq 工具，并概述了它们的用户界面和功能。然后，详细描述了一个从开始到结束的综合 scRNA-seq 分析流程，旨在使研究人员能够进行 scRNA-seq 分析，无论其计算经验如何。配套的视频教程可在 YouTube 上的“EasyScRNAseqTutorials”（https://www.youtube.com/@scrnaseqtutorials）找到。然而，随着 scRNA-seq 继续渗透到新的领域并扩大其重要性，仍然需要更多的文献来帮助克服使用它的障碍，进一步解释本文中引入的高度复杂和先进的分析。
