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.

中文翻译：

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设计的实验数据的数据融合中的共同而不同的变化。

简介多个数据集的综合分析可以提供有关所研究的生物系统的补充信息。但是，由于潜在的实验因素，数据集可能包含不同的变异来源，因此多个生物学数据集的数据融合可能会变得很复杂。因此，在数据融合概念中考虑数据集的实验设计可能很重要。目的在目前的工作中，我们旨在将实验设计信息纳入对多个设计数据集的综合分析中。方法在这里，我们描述了惩罚指数方差分析同时成分分析（PE-ASCA），这是一种用于对来自多个隔间或具有相同基础实验设计的分析平台的数据集进行综合分析的新方法。结果使用两个模拟案例，将同时成分分析（SCA），惩罚性指数同时成分分析（P-ESCA）和方差分析同时成分分析（ASCA）的结果与所提出的方法进行了比较。此外，通过PE-ASCA研究了来自相同仔猪的两个不同大脑组织（下丘脑和中脑）的NMR分析得到的真实代谢组学数据，并进行了基础实验设计。结论该方法可以更好地理解响应不同实验因素的共同变化和独特变化。通过PE-ASCA研究了来自同一个仔猪的两个不同大脑组织（下丘脑和中脑）的NMR分析得到的真实代谢组学数据，并进行了基础实验设计。结论该方法可以更好地理解响应不同实验因素的共同和独特的变化。通过PE-ASCA研究了来自同一个仔猪的两个不同大脑组织（下丘脑和中脑）的NMR分析得到的真实代谢组学数据，并进行了基础实验设计。结论该方法可以更好地理解响应不同实验因素的共同和独特的变化。