Simplivariate Models: Uncovering the Underlying Biology in Functional Genomics Data

Edoardo Saccenti, Johan A Westerhuis, Age K Smilde, Mariët J van der Werf, Jos A Hageman and Margriet M W B Hendriks

PLOS ONE, 2011, vol. 6, issue 6, 1-13

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.

Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0020747

DOI: 10.1371/journal.pone.0020747

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