Common and distinct variation in data fusion of designed experimental data

Masoumeh Alinaghihossein, Hanne Christine S. Bertram, Anders Brunse, Age Klaas Smilde, Johan A. Westerhuis

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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.

Original languageEnglish
Article number2
Publication statusPublished - Jan 2020


  • ANOVA-simultaneous component analysis (ASCA)
  • Concave penalty
  • Data integration
  • Multiset data analysis
  • NMR metabolomics


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