A View from Above: Cloud Plots to Visualize Global Metabolomic Data

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• 作者：Gary J. Patti ; Ralf Tautenhahn ; Duane Rinehart ; Kevin Cho ; Leah P. Shriver ; Marianne Manchester ; Igor Nikolskiy ; Caroline H. Johnson ; Nathaniel G. Mahieu ; Gary Siuzdak
• 刊名：Analytical Chemistry
• 出版年：2013
• 出版时间：January 15, 2013
• 年：2013
• 卷：85
• 期：2
• 页码：798-804
• 全文大小：406K
• 年卷期：v.85,no.2(January 15, 2013)
• ISSN：1520-6882

文摘

Global metabolomics describes the comprehensive analysis of small molecules in a biological system without bias. With mass spectrometry-based methods, global metabolomic data sets typically comprise thousands of peaks, each of which is associated with a mass-to-charge ratio, retention time, fold change, p-value, and relative intensity. Although several visualization schemes have been used for metabolomic data, most commonly used representations exclude important data dimensions and therefore limit interpretation of global data sets. Given that metabolite identification through tandem mass spectrometry data acquisition is a time-limiting step of the untargeted metabolomic workflow, simultaneous visualization of these parameters from large sets of data could facilitate compound identification and data interpretation. Here, we present such a visualization scheme of global metabolomic data using a so-called 鈥渃loud plot鈥?to represent multidimensional data from septic mice. While much attention has been dedicated to lipid compounds as potential biomarkers for sepsis, the cloud plot shows that alterations in hydrophilic metabolites may provide an early signature of the disease prior to the onset of clinical symptoms. The cloud plot is an effective representation of global mass spectrometry-based metabolomic data, and we describe how to extract it as standard output from our XCMS metabolomic software.