Discrimination models using variance-stabilizing transformation of metabolomic NMR data
Discrimination models using variance-stabilizing transformation of metabolomic NMR data
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DOI：
10.1089/1536231041388348
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发表时间：
2004-06-01
影响因子：
3.3
通讯作者：
Woodruff, DL
中科院分区：
文献类型：
--
作者：
Purohit, PV;Rocke, DM;Woodruff, DL
在基因组学、蛋白质组学和代谢组学领域进行了大量工作之后，代谢物的研究本身就引起了人们的兴趣。生物系统中的代谢酶使人们了解系统的状态，并为疾病和其他疾病的研究提供了有力的工具。几种分析技术，如质谱和高分辨率NMR光谱已被用于研究代谢物。然而，这些技术的数据仍然非常复杂。传统上，多变量分析已用于此类数据。然而，这些方法有一个基本假设，即数据是具有恒定方差的多元正态分布。但情况未必如此。已经表明，广义对数变换使得数据的方差有效地恒定，使得数据更适合于多变量分析。我们证明了这些转换的有效性，对一组18个鲍鱼被归类为健康，发育不良，或患病的NMR数据。我们展示了这种转换如何使鲍鱼的健康、发育不良和患病类别的多变量分类更加有效，并为识别潜在的代谢生物标志物提供了一种工具;用于疾病。
After the extensive work that is being done in the areas of genomics, proteomics, and metabolomics, the study of metabolites has come of interest in its own right. Metabolites in biological systems give an understanding of the state of the system and provide a powerful tool for the study of disease and other maladies. Several analytical techniques such as mass spectrometry and high-resolution NMR spectroscopy have been used to study metabolites. The data, however, from these techniques remains quite complex. Traditionally, multivariate analyses have been used for such data. These methods however have an underlying assumption that the data is multivariate normal with a constant variance. This is not necessarily the case. It has been shown that a generalized log transformation renders the variance of the data constant effectively making the data more suitable for multivariate analysis. We demonstrate the effectiveness of these transformations on NMR data taken on a set of 18 abalone that were categorized as either being healthy, stunted, or diseased. We show how the transformation makes multivariate classification of the abalone into the healthy, stunted and diseased categories much more effective and gives a tool for identifying potential metabolic biomarkers; for disease.