Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, China.
Univeristy of Chinese Academy of Sciences, Beijing, China.
Bioinformatics. 2019 Aug 15;35(16):2870-2872. doi: 10.1093/bioinformatics/bty1066.
Mass spectrometry-based metabolomics aims to profile the metabolic changes in biological systems and identify differential metabolites related to physiological phenotypes and aberrant activities. However, many confounding factors during data acquisition complicate metabolomics data, which is characterized by high dimensionality, uncertain degrees of missing and zero values, nonlinearity, unwanted variations and non-normality. Therefore, prior to differential metabolite discovery analysis, various types of data cleaning such as batch alignment, missing value imputation, data normalization and scaling are essentially required for data post-processing. Here, we developed an interactive web server, namely, MetFlow, to provide an integrated and comprehensive workflow for metabolomics data cleaning and differential metabolite discovery.
The MetFlow is freely available on http://metflow.zhulab.cn/.
Supplementary data are available at Bioinformatics online.
基于质谱的代谢组学旨在分析生物系统中的代谢变化，并鉴定与生理表型和异常活动相关的差异代谢物。然而，数据采集过程中的许多混杂因素使代谢组学数据变得复杂，其具有高维性、缺失值和零值的不确定程度、非线性、不需要的变化和非正态性等特点。因此，在进行差异代谢物发现分析之前，需要对数据进行各种类型的数据清理，如批次对齐、缺失值插补、数据标准化和缩放等，这是数据后处理的基本步骤。在这里，我们开发了一个交互式网络服务器，即 MetFlow，为代谢组学数据清理和差异代谢物发现提供了一个集成和全面的工作流程。
MetFlow 可在 http://metflow.zhulab.cn/ 上免费获得。
补充数据可在生物信息学在线获得。
