Common and distinct variation in data fusion of designed experimental data
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Common and distinct variation in data fusion of designed experimental data. / Alinaghi, Masoumeh; Bertram, Hanne Christine; Brunse, Anders; Smilde, Age K.; Westerhuis, Johan A.
I: Metabolomics, Bind 16, Nr. 1, 2, 2020.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Common and distinct variation in data fusion of designed experimental data
AU - Alinaghi, Masoumeh
AU - Bertram, Hanne Christine
AU - Brunse, Anders
AU - Smilde, Age K.
AU - Westerhuis, Johan A.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - ANOVA-simultaneous component analysis (ASCA)
KW - Concave penalty
KW - Data integration
KW - Multiset data analysis
KW - NMR metabolomics
U2 - 10.1007/s11306-019-1622-2
DO - 10.1007/s11306-019-1622-2
M3 - Journal article
C2 - 31797165
AN - SCOPUS:85075918525
VL - 16
JO - Metabolomics
JF - Metabolomics
SN - 1573-3882
IS - 1
M1 - 2
ER -
ID: 234209488