Using MetaboAnalyst 4.0 for Comprehensive and Integrative Metabolomics Data Analysis
Q1 Biochemistry, Genetics and Molecular Biology
Jasmine Chong, David S. Wishart, Jianguo Xia
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Abstract
MetaboAnalyst (https://www.metaboanalyst.ca) is an easy‐to‐use web‐based tool suite for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since its first release in 2009, MetaboAnalyst has evolved significantly to meet the ever‐expanding bioinformatics demands from the rapidly growing metabolomics community. In addition to providing a variety of data processing and normalization procedures, MetaboAnalyst supports a wide array of functions for statistical, functional, as well as data visualization tasks. Some of the most widely used approaches include PCA (principal component analysis), PLS‐DA (partial least squares discriminant analysis), clustering analysis and visualization, MSEA (metabolite set enrichment analysis), MetPA (metabolic pathway analysis), biomarker selection via ROC (receiver operating characteristic) curve analysis, as well as time series and power analysis. The current version of MetaboAnalyst (4.0) features a complete overhaul of the user interface and significantly expanded underlying knowledge bases (compound database, pathway libraries, and metabolite sets). Three new modules have been added to support pathway activity prediction directly from mass peaks, biomarker meta‐analysis, and network‐based multi‐omics data integration. To enable more transparent and reproducible analysis of metabolomic data, we have released a companion R package (MetaboAnalystR) to complement the web‐based application. This article provides an overview of the main functional modules and the general workflow of MetaboAnalyst 4.0, followed by 12 detailed protocols: © 2019 by John Wiley & Sons, Inc.
使用MetaboAnalyst 4.0进行综合代谢组学数据分析
MetaboAnalyst (https://www.metaboanalyst.ca)是一个易于使用的基于web的工具套件,用于全面的代谢组学数据分析,解释和与其他组学数据的集成。自2009年首次发布以来,MetaboAnalyst已经显著发展,以满足快速增长的代谢组学社区不断扩大的生物信息学需求。除了提供各种数据处理和规范化过程之外,MetaboAnalyst还支持用于统计、函数和数据可视化任务的各种功能。一些最广泛使用的方法包括PCA(主成分分析),PLS-DA(偏最小二乘判别分析),聚类分析和可视化,MSEA(代谢物集富集分析),MetPA(代谢途径分析),通过ROC(受试者工作特征)曲线分析进行生物标志物选择,以及时间序列和功率分析。当前版本的MetaboAnalyst(4.0)对用户界面进行了全面修改,并显著扩展了基础知识库(化合物数据库、途径库和代谢物集)。增加了三个新模块,以支持直接从质量峰,生物标志物荟萃分析和基于网络的多组学数据集成的途径活性预测。为了使代谢组学数据的分析更加透明和可重复,我们发布了一个配套的R包(MetaboAnalystR)来补充基于web的应用程序。本文概述了MetaboAnalyst 4.0的主要功能模块和一般工作流程,其次是12个详细协议:©2019 by John Wiley &基本方案1:数据上传、处理和归一化基本方案2:显著变量的识别基本方案3:多变量探索性数据分析基本方案4:代谢组学的功能解释数据库方案5:基于受试者工作特征(ROC)曲线的生物标志物分析基本方案6:时间序列和双因素数据分析基本方案7:样本量估计和功率分析基本方案8:联合通路分析基本协议9:MS峰到通路活性基本协议10:生物标志物元分析基本协议11:基于知识的多组学网络探索基本协议12:代谢分析介绍
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