Research Summary: 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.

____________________________

Publisher: Public Library of Science

Date Published: 16-June-2011

Author(s): Saccenti E., Westerhuis J., Smilde A., van der Werf M., Hageman J., Hendriks M.

Link: https://doi.org/10.1371/journal.pone.0020747

Leave a Reply