Ludt Annekathrin, Ustjanzew Arsenij, Binder Harald, Strauch Konstantin, Marini Federico
Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), Division Statistical Genomics and Bioinformatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg im Breisgau, Germany.
Curr Protoc. 2022 Apr;2(4):e411. doi: 10.1002/cpz1.411.
The generation and interpretation of results from transcriptome profiling experiments via RNA sequencing (RNA-seq) can be a complex task. While raw data quality control, alignment, and quantification can be streamlined via efficient algorithms that can deliver the preprocessed expression matrix, a common bottleneck in the analysis of such large datasets is the subsequent in-depth, iterative processes of data exploration, statistical testing, visualization, and interpretation. Specific tools for these workflow steps are available but require a level of technical expertise which might be prohibitive for life and clinical scientists, who are left with essential pieces of information distributed among different tabular and list formats. Our protocols are centered on the joint use of our Bioconductor packages (pcaExplorer, ideal, GeneTonic) for interactive and reproducible workflows. All our packages provide an interactive and accessible experience via Shiny web applications, while still documenting the steps performed with RMarkdown as a framework to guarantee the reproducibility of the analyses, reducing the overall time to generate insights from the data at hand. These protocols guide readers through the essential steps of Exploratory Data Analysis, statistical testing, and functional enrichment analyses, followed by integration and contextualization of results. In our packages, the core elements are linked together in interactive widgets that make drill-down tasks efficient by viewing the data at a level of increased detail. Thanks to their interoperability with essential classes and gold-standard pipelines implemented in the open-source Bioconductor project and community, these protocols will permit complex tasks in RNA-seq data analysis, combining interactivity and reproducibility for following modern best scientific practices and helping to streamline the discovery process for transcriptome data. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Exploratory Data Analysis with pcaExplorer Basic Protocol 2: Differential Expression Analysis with ideal Basic Protocol 3: Interpretation of RNA-seq results with GeneTonic Support Protocol: Downloading and installing pcaExplorer, ideal, and GeneTonic Alternate Protocol: Using functions from pcaExplorer, ideal, and GeneTonic in custom analyses.
通过RNA测序（RNA-seq）进行转录组分析实验结果的生成与解读是一项复杂的任务。虽然原始数据的质量控制、比对和定量可通过能提供预处理表达矩阵的高效算法来简化流程，但分析此类大型数据集时，一个常见的瓶颈在于后续深入、迭代的数据探索、统计检验、可视化和解读过程。针对这些工作流程步骤有特定的工具可用，但需要一定的技术专长，这对生命科学和临床科学家来说可能具有挑战性，因为他们面对的重要信息分散在不同的表格和列表格式中。我们的方案以联合使用Bioconductor软件包（pcaExplorer、ideal、GeneTonic）进行交互式和可重复的工作流程为核心。我们所有的软件包都通过Shiny网络应用程序提供交互式且易于访问的体验，同时仍以RMarkdown为框架记录执行的步骤，以确保分析的可重复性，减少从现有数据生成见解的总体时间。这些方案引导读者完成探索性数据分析、统计检验和功能富集分析的基本步骤，随后对结果进行整合和情境化。在我们的软件包中，核心元素通过交互式小部件链接在一起，通过以更高的细节级别查看数据，使深入研究任务变得高效。由于它们与开源Bioconductor项目和社区中实现的基本类和金标准管道具有互操作性，这些方案将允许在RNA-seq数据分析中执行复杂任务，将交互性和可重复性相结合，以遵循现代最佳科学实践，并有助于简化转录组数据的发现过程。© 2022作者。由Wiley Periodicals LLC出版的《当前方案》。基本方案1：使用pcaExplorer进行探索性数据分析 基本方案2：使用ideal进行差异表达分析 基本方案3：使用GeneTonic解读RNA-seq结果 支持方案：下载并安装pcaExplorer、ideal和GeneTonic 替代方案：在自定义分析中使用pcaExplorer、ideal和GeneTonic的函数
