limpca:一个基于ASCA/APCA系列方法的高维设计数据线性建模的R软件包

IF 2.3 4区 化学 Q1 SOCIAL WORK
Michel Thiel, Nadia Benaiche, Manon Martin, Sébastien Franceschini, Robin Van Oirbeek, Bernadette Govaerts
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引用次数: 0

摘要

许多现代分析方法用于分析从实验设计中发出的样本,例如,在医学、生物、化学或农学领域。这些方法在大多数情况下生成高度多元的数据,如光谱或图像,其中变量的数量(描述符响应)往往远大于实验单元的数量。因此,有必要使用多变量统计工具来识别持续受实验因素影响的变量。在这种情况下,开发了两种结合ANOVA和PCA的最新方法,即ASCA(ANOVA‐同时成分分析)和APCA(ANOVA-主成分分析)。它们为与实验设计相关的统计模型的每个效应空间中的多元结构提供了强大的可视化工具。它们的主要局限性在于,当实验设计不平衡时,它们会产生因子效应的有偏估计。本文介绍了R包limpca(用于具有主成分效应分析的线性模型),它实现了ASCA+和APCA+,这是ASCA和APCA方法的增强版本,基于一般线性模型(GLM)理论的几个原理。本文综述了该方法,介绍了包装结构和功能,并使用代谢组学数据集来清楚地证明ASCA+和APCA+方法的潜力,以突出与不平衡设计中感兴趣的效果相对应的真实生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
limpca: An R package for the linear modeling of high-dimensional designed data based on ASCA/APCA family of methods

Many modern analytical methods are used to analyze samples issued from an experimental design, for example, in medical, biological, chemical, or agronomic fields. Those methods generate most of the time, highly multivariate data like spectra or images, where the number of variables (descriptor responses) tends to be much larger than the number of experimental units. Therefore, multivariate statistical tools are necessary to identify variables that are consistently affected by experimental factors. In this context, two recent methods combining ANOVA and PCA, namely, ASCA (ANOVA-Simultaneous Component Analysis) and APCA (ANOVA-Principal Component Analysis), were developed. They provide powerful visualization tools for multivariate structures in the space of each effect of the statistical model linked to the experimental design. Their main limitation is that they produce biased estimators of the factor effects when the design of experiment is unbalanced. This article presents the R package limpca (for linear models with principal component effects analysis) that implements ASCA+ and APCA+, an enhanced version of ASCA and APCA methods based on several principles from the theory of general linear models (GLM). In this paper, the methodology is reviewed, the package structure and functions are presented, and a metabolomics data set is used to clearly demonstrate the potential of ASCA+ and APCA+ methods to highlight true biomarkers corresponding to effects of interest in unbalanced designs.

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来源期刊
Journal of Chemometrics
Journal of Chemometrics 化学-分析化学
CiteScore
5.20
自引率
8.30%
发文量
78
审稿时长
2 months
期刊介绍: The Journal of Chemometrics is devoted to the rapid publication of original scientific papers, reviews and short communications on fundamental and applied aspects of chemometrics. It also provides a forum for the exchange of information on meetings and other news relevant to the growing community of scientists who are interested in chemometrics and its applications. Short, critical review papers are a particularly important feature of the journal, in view of the multidisciplinary readership at which it is aimed.
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