Burton Lyle, Ivosev Gordana, Tate Stephen, Impey Gary, Wingate Julie, Bonner Ron
Applied Biosystems/MDS Sciex, 71 Four Valley Drive, Concord, Ontario, Canada.
J Chromatogr B Analyt Technol Biomed Life Sci. 2008 Aug 15;871(2):227-35. doi: 10.1016/j.jchromb.2008.04.044. Epub 2008 May 10.
The experimental complexity of a metabolomics study can cause uncontrolled variance that is not related to the biological effect being studied and may distort or obscure the data analysis. While some sources can be controlled with good experimental techniques and careful sample handling, others are inherent in the analytical technique used and cannot easily be avoided. We discuss the sources and appearance of some of these artifacts and show ways in which they can be detected using visualization and statistical tools, allowing appropriate treatment prior to multivariate analysis (MVA).
代谢组学研究的实验复杂性可能会导致与所研究的生物学效应无关的失控方差,从而可能扭曲或掩盖数据分析结果。虽然一些来源可以通过良好的实验技术和仔细的样本处理加以控制,但其他一些来源则是所用分析技术所固有的,难以轻易避免。我们讨论了其中一些假象的来源和表现形式,并展示了如何使用可视化和统计工具来检测它们,以便在多变量分析(MVA)之前进行适当处理。