title: Simplivariate models: uncovering the underlying biology in functional genomics data.
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.
authors.affiliation: Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands. e.saccenti@uva.nl
authors.forename: Edoardo
authors.initials: E
authors.lastname: Saccenti
authors.forename: Johan A
authors.initials: JA
authors.lastname: Westerhuis
authors.forename: Age K
authors.initials: AK
authors.lastname: Smilde
authors.forename: Mariët J
authors.initials: MJ
authors.lastname: van der Werf
authors.forename: Jos A
authors.initials: JA
authors.lastname: Hageman
authors.forename: Margriet M W B
authors.initials: MM
authors.lastname: Hendriks
mesh_terms: D000465:Algorithms; D004926:Escherichia coli / Q000235:genetics; D008401:Gas Chromatography-Mass Spectrometry; D016680:Genome, Bacterial; D023281:Genomics; D009682:Magnetic Resonance Spectroscopy; D055432:Metabolomics; D008957:Models, Genetic; D012984:Software