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- W2133298075 abstract "One of the first steps in analyzing high-dimensional functional genomics data is an exploratory analysis of such data. Cluster Analysis and Principal Component Analysis are then usually the method of choice. Despite their versatility they also have a severe drawback: they do not always generate simple and interpretable solutions. On the basis of the observation that functional genomics data often contain both informative and non-informative variation, we propose a method that finds sets of variables containing informative variation. This informative variation is subsequently expressed in easily interpretable simplivariate components.We present a new implementation of the recently introduced simplivariate models. In this implementation, the informative variation is described by multiplicative models that can adequately represent the relations between functional genomics data. Both a simulated and two real-life metabolomics data sets show good performance of the method." @default.
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- W2133298075 date "2011-06-16" @default.
- W2133298075 modified "2023-10-07" @default.
- W2133298075 title "Simplivariate Models: Uncovering the Underlying Biology in Functional Genomics Data" @default.
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- W2133298075 doi "https://doi.org/10.1371/journal.pone.0020747" @default.
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