Common and distinct variation in data fusion of designed experimental data
Common and distinct variation in data fusion of designed experimental data
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DOI：
10.1007/s11306-019-1622-2
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发表时间：
2019-12-03
期刊：
影响因子：
3.6
通讯作者：
Westerhuis, Johan A.
中科院分区：
文献类型：
--
作者：
Alinaghi, Masoumeh;Bertram, Hanne Christine;Westerhuis, Johan A.
对多个数据集进行综合分析可以为所研究的生物系统提供补充信息。然而，多个生物数据集的数据融合可能会很复杂，因为数据集可能由于潜在的实验因素而包含不同的变异来源。因此，在数据融合概念中考虑数据集的实验设计是非常重要的。在目前的工作中，我们的目标是将实验设计信息纳入多个设计数据集的综合分析中。在这里，我们描述了惩罚指数方差分析同时成分分析（PE-ASCA），这是一种综合分析来自多个隔间或分析平台的数据集的新方法，具有相同的基础实验设计。结果通过两个模拟案例，对比了同时成分分析（SCA）、惩罚指数同时成分分析（P-ESCA）和方差分析（anova）同时成分分析（ASCA）的结果。此外，采用PE-ASCA对同一头仔猪两种不同脑组织（下丘脑和中脑）的核磁共振分析获得的真实代谢组学数据进行了研究。结论该方法可以更好地了解不同实验因素的共同和独特变化。
Introduction Integrative analysis of multiple data sets can provide complementary information about the studied biological system. However, data fusion of multiple biological data sets can be complicated as data sets might contain different sources of variation due to underlying experimental factors. Therefore, taking the experimental design of data sets into account could be of importance in data fusion concept.Objectives In the present work, we aim to incorporate the experimental design information in the integrative analysis of multiple designed data sets.Methods Here we describe penalized exponential ANOVA simultaneous component analysis (PE-ASCA), a new method for integrative analysis of data sets from multiple compartments or analytical platforms with the same underlying experimental design.Results Using two simulated cases, the result of simultaneous component analysis (SCA), penalized exponential simultaneous component analysis (P-ESCA) and ANOVA-simultaneous component analysis (ASCA) are compared with the proposed method. Furthermore, real metabolomics data obtained from NMR analysis of two different brains tissues (hypothalamus and midbrain) from the same piglets with an underlying experimental design is investigated by PE-ASCA.Conclusions This method provides an improved understanding of the common and distinct variation in response to different experimental factors.