Institute of Parasitology, McGill University, Montreal, Quebec, Canada.
Department of Microbiology and Immunology, McGill University, Montreal, Quebec, Canada.
Nat Protoc. 2024 May;19(5):1467-1497. doi: 10.1038/s41596-023-00950-4. Epub 2024 Feb 14.
The growing number of multi-omics studies demands clear conceptual workflows coupled with easy-to-use software tools to facilitate data analysis and interpretation. This protocol covers three key components involved in multi-omics analysis, including single-omics data analysis, knowledge-driven integration using biological networks and data-driven integration through joint dimensionality reduction. Using the dataset from a recent multi-omics study of human pancreatic islet tissue and plasma samples, the first section introduces how to perform transcriptomics/proteomics data analysis using ExpressAnalyst and lipidomics data analysis using MetaboAnalyst. On the basis of significant features detected in these workflows, the second section demonstrates how to perform knowledge-driven integration using OmicsNet. The last section illustrates how to perform data-driven integration from the normalized omics data and metadata using OmicsAnalyst. The complete protocol can be executed in ~2 h. Compared with other available options for multi-omics integration, the Analyst software suite described in this protocol enables researchers to perform a wide range of omics data analysis tasks via a user-friendly web interface.
越来越多的多组学研究需要明确的概念工作流程,以及易于使用的软件工具,以方便数据分析和解释。本方案涵盖了多组学分析涉及的三个关键组成部分,包括单组学数据分析、基于生物学网络的知识驱动整合以及通过联合降维实现的数据驱动整合。本方案使用了最近一项人类胰岛组织和血浆样本的多组学研究的数据集,第一节介绍了如何使用 ExpressAnalyst 进行转录组学/蛋白质组学数据分析,以及如何使用 MetaboAnalyst 进行脂质组学数据分析。在这些工作流程中检测到显著特征的基础上,第二节展示了如何使用 OmicsNet 进行知识驱动整合。最后一节说明了如何使用 OmicsAnalyst 从归一化的组学数据和元数据中进行数据驱动整合。整个方案大约可以在 2 小时内完成。与其他可用的多组学整合选项相比,本方案中描述的 Analyst 软件套件通过用户友好的网络界面使研究人员能够执行广泛的组学数据分析任务。