CorrelationCalculator 和 Filigree：用于代谢组学数据的基于数据驱动的网络分析的工具。
CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data.
机构信息
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor.
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor; Taubman Health Sciences Library, University of Michigan, Ann Arbor.
出版信息
J Vis Exp. 2023 Nov 10(201). doi: 10.3791/65512.
A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of relating changes in levels of individual metabolites to specific biological processes is compounded by the large number of unknown metabolites present in untargeted liquid chromatography-mass spectrometry (LC-MS) studies. Further, secondary metabolism and lipid metabolism are poorly represented in existing pathway databases. To overcome these limitations, our group has developed several tools for data-driven network construction and analysis. These include CorrelationCalculator and Filigree. Both tools allow users to build partial correlation-based networks from experimental metabolomics data when the number of metabolites exceeds the number of samples. CorrelationCalculator supports the construction of a single network, while Filigree allows building a differential network utilizing data from two groups of samples, followed by network clustering and enrichment analysis. We will describe the utility and application of both tools for the analysis of real-life metabolomics data.
在分析组学数据时，面临的一个重大挑战是提取可操作的生物学知识。代谢组学也不例外。在非靶向液相色谱-质谱（LC-MS）研究中，存在大量未知代谢物，这使得将个体代谢物水平的变化与特定生物过程相关联这一普遍问题更加复杂。此外，现有途径数据库中对次生代谢和脂质代谢的描述也很少。为了克服这些限制，我们的研究小组开发了几种用于数据驱动的网络构建和分析的工具。其中包括 CorrelationCalculator 和 Filigree。这两个工具都允许用户在代谢组学数据中的代谢物数量超过样本数量时，基于部分相关构建网络。CorrelationCalculator 支持构建单个网络，而 Filigree 则允许利用两组样本的数据构建差异网络，然后对网络进行聚类和富集分析。我们将描述这两个工具在分析实际代谢组学数据中的应用和实用性。
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