Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data.
Optimized Phenotypic Biomarker Discovery and Confounder Elimination via Covariate-Adjusted Projection to Latent Structures from Metabolic Spectroscopy Data.
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DOI：
10.1021/acs.jproteome.7b00879
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发表时间：
2018-04-06
影响因子：
4.4
通讯作者：
Nicholson JK
中科院分区：
文献类型：
--
作者：
Posma JM;Garcia-Perez I;Ebbels TMD;Lindon JC;Stamler J;Elliott P;Holmes E;Nicholson JK
代谢被遗传、饮食、疾病状态、环境和许多其他因素改变。对其中任何一个进行建模通常都不考虑其他协变量的影响。将代谢特征的差异归因于这些因素之一，需要在控制其余因素的代谢影响的同时进行。我们在这里描述了一个数据分析框架和新的混杂调整算法的多变量分析代谢分析数据。使用模拟数据，我们发现，类似数量的真实关联和显着减少误报相比，其他常用的方法。协变量调整的预测潜在结构（CA-PLS）的例子在这里使用大规模的代谢表型研究的两个中国人群在不同的心血管疾病的风险。使用CA-PLS，我们发现一些以前报道的差异实际上与外部因素有关，并发现了一些以前未报道的与不同代谢途径相关的生物标志物。CA-PLS可以应用于任何多变量数据，其中混淆可能是一个问题，并且混淆调整过程可转换为其他多变量回归技术。
Metabolism is altered by genetics, diet, disease status, environment, and many other factors. Modeling either one of these is often done without considering the effects of the other covariates. Attributing differences in metabolic profile to one of these factors needs to be done while controlling for the metabolic influence of the rest. We describe here a data analysis framework and novel confounder-adjustment algorithm for multivariate analysis of metabolic profiling data. Using simulated data, we show that similar numbers of true associations and significantly less false positives are found compared to other commonly used methods. Covariate-adjusted projections to latent structures (CA-PLS) are exemplified here using a large-scale metabolic phenotyping study of two Chinese populations at different risks for cardiovascular disease. Using CA-PLS, we find that some previously reported differences are actually associated with external factors and discover a number of previously unreported biomarkers linked to different metabolic pathways. CA-PLS can be applied to any multivariate data where confounding may be an issue and the confounder-adjustment procedure is translatable to other multivariate regression techniques.
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DOI：
10.1016/j.chemolab.2006.04.021
发表时间：
2006-12-01
影响因子：
3.9
作者：
Anderssen, Endre;Dyrstad, Knut;Martens, Harald
通讯作者：
Martens, Harald
影响因子：
5.8
作者：
Chakraborty, Sutirtha;Datta, Somnath;Datta, Susmita
通讯作者：
Datta, Susmita
影响因子：
4.2
作者：
Go, VLW;Nguyen, CTH;Lee, WNP
通讯作者：
Lee, WNP
DOI：
10.1152/ajprenal.1995.268.6.f983
发表时间：
1995-06-01
期刊：
AMERICAN JOURNAL OF PHYSIOLOGY-RENAL FLUID AND ELECTROLYTE PHYSIOLOGY
影响因子：
--
作者：
BURG, MB
通讯作者：
BURG, MB
DOI：
10.1017/s0962492904000236
发表时间：
2005-01-01
期刊：
ACTA NUMERICA, VOL 14, 2005
影响因子：
--
作者：
Edelman, A;Rao, NR
通讯作者：
Rao, NR