Simplivariate Models: Uncovering the Underlying Biology in Functional Genomics Data

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.

Bibliographic Details

Main Authors:	Saccenti, E., Westerhuis, J.A., Smilde, A.K., van der Werf, M.J., Hageman, J.A., Hendriks, M.M.W.B.
Format:	Article/Letter to editor biblioteca
Language:	English
Subjects:	decomposition, escherichia-coli, genetic algorithms, indole, metabolomics data, microarray data, multiple-regression, number,
Online Access:	https://research.wur.nl/en/publications/simplivariate-models-uncovering-the-underlying-biology-in-functio
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