2005
DOI: 10.1093/bioinformatics/bti476
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ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data

Abstract: We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors.

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Cited by 573 publications
(470 citation statements)
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“…ICA is another useful method in metabolomics, as component selection is not as critical and it allows the user to disregard technical variation in MS data, thereby improving the results of the final analysis (Scholz et al, 2004). However, it is often more effective to use several different methods together (Smilde et al, 2005;Vis et al, 2007).…”
Section: Discussionmentioning
confidence: 99%
“…ICA is another useful method in metabolomics, as component selection is not as critical and it allows the user to disregard technical variation in MS data, thereby improving the results of the final analysis (Scholz et al, 2004). However, it is often more effective to use several different methods together (Smilde et al, 2005;Vis et al, 2007).…”
Section: Discussionmentioning
confidence: 99%
“…Subsequently, it builds SCA models for each type of variation. This makes the interpretation of the models much easier [74]. Interestingly, a similar method was developed independently in the field of proteomics [75].…”
Section: Data Analysis Applying Three-way Analysismentioning
confidence: 99%
“…To cope with multi-colinearity inherent in metabolomics data [60,61], the AoV-PLS 5 procedure proposed by El Ghaziri et al [62] was applied.…”
mentioning
confidence: 99%