Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification.
Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification.
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DOI：
10.1021/pr1003449
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发表时间：
2010-09-03
影响因子：
4.4
通讯作者：
De Iorio, Maria
中科院分区：
文献类型：
--
作者：
Chadeau-Hyam, Marc;Ebbels, Timothy M. D.;Brown, Ian J.;Chan, Queenie;Stemler, Jeremiah;Huang, Chiang Ching;Daviglus, Martha L.;Ueshima, Hirotsugu;Zhao, Liancheng;Holmes, Elaine;Nicholson, Jeremy K.;Elliott, Paul;De Iorio, Maria
通过全代谢组关联研究（MWAS）的高通量代谢谱分析是鉴定疾病风险的生物标志物的一种强有力的新方法，但存在方法学挑战：高维数、高水平的共线性、代谢谱数据内峰重叠的存在、多重检验和合适的显著性阈值的选择。我们定义的代谢组范围内的显着性水平（MWSL）的阈值需要通过排列的方法来控制家庭明智的错误率。我们使用来自INTERMAP研究的24小时尿液收集物的1H NMR光谱图。我们的研究结果表明，MWSL主要取决于样本大小和光谱分辨率。MWSL估计值可用于指导MWA研究中区分性生物标志物的选择。在模拟研究中，我们比较统计性能的MWSL方法的两个变种的正交偏最小二乘法（OPLS）的统计功率，假阳性率和对应的排名最显着的光谱变量。我们的研究结果表明，MWSL的方法估计的单变量t-检验是不优于OPLS，并提供了一种快速，简单的方法来检测疾病相关的歧视性特征，在人类核磁共振尿代谢谱。
High throughput metabolic profiling via the metabolome-wide association study (MWAS) is a powerful new approach to identify biomarkers of disease risk, but there are methodological challenges: high dimensionality, high level of collinearity, the existence of peak overlap within metabolic spectral data, multiple testing and selection of a suitable significance threshold. We define the metabolome-wide significance level (MWSL) as the threshold required to control the family wise error rate through a permutation approach. We used 1H NMR spectroscopic profiles of 24 hour urinary collections from the INTERMAP study. Our results show that the MWSL primarily depends on sample size and spectral resolution. The MWSL estimates can be used to guide selection of discriminatory biomarkers in MWA studies. In a simulation study, we compare statistical performance of the MWSL approach to two variants of orthogonal partial least squares (OPLS) method with respect to statistical power, false positive rate and correspondence of ranking of the most significant spectral variables. Our results show that the MWSL approach as estimated by the univariate t-test is not outperformed by OPLS and offers a fast and simple method to detect disease-related discriminatory features in human NMR urinary metabolic profiles.
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影响因子：
9.8
作者：
Dudbridge, F;Koeleman, BPC
通讯作者：
Koeleman, BPC
影响因子：
2.7
作者：
Kulinskaya, Elena;Lewin, Alex
通讯作者：
Lewin, Alex
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
2.7
作者：
SIMES, RJ
通讯作者：
SIMES, RJ
影响因子：
2.4
作者：
Trygg, J;Wold, S
通讯作者：
Wold, S