Analysis of variance-principal component analysis: A soft tool for proteomic discovery
Analysis of variance-principal component analysis: A soft tool for proteomic discovery
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DOI：
10.1016/j.aca.2005.02.042
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发表时间：
2005-07-15
影响因子：
6.2
通讯作者：
Yergey, AL
中科院分区：
文献类型：
--
作者：
Harrington, PD;Vieira, NE;Yergey, AL
开发了一个在高维数据集中检测生物标志物的软件工具。该工具结合了方差分析（ANOVA）和主成分分析（PCA）。使用ANOVA将协变分为主效应和交互作用。将每个效应的协方差与纯误差组合并进行PCA。如果主效应与残差相比显著，则第一个主成分将跨越此变异源。这种技术避免了主成分的旋转，并且当变量载荷显著时，可以进行解释。ANOVA-PCA被证明是一种优化生物标志物蛋白质组学测定的工具。两组独立的基质辅助激光解吸/电离质谱（MALDI-MS）收集从ammotic流体。这些研究为早产提供了一致的生物标志物。(c)2005 Elsevier B. V.保留所有权利。
A soft tool for detection of biomarkers in high dimensional data sets has been developed. The tool combines analysis of variance (ANOVA) and principal component analysis (PCA). Covariations are separated using ANOVA into main effects and interaction. The covariances for each effect are combined with the pure error and subjected to PCA. If the main effect is significant compared to the residual error, the first principal component will span this source of variation. This technique avoids rotation of the principal components and when significant the variable loadings are amenable to interpretation. ANOVA-PCA is demonstrated as a tool for optimization of a proteomic assay for biomarkers. Two independent sets of matrix assisted laser desorption/ionization-mass spectra (MALDI-MS) were collected from ammotic fluids. These studies gave consistent biomarkers for premature delivery. (c) 2005 Elsevier B.V. All rights reserved.