Integrative analysis of metabolomics and transcriptomics data: a unified model framework to identify underlying system pathways.

ABSTRACT: The abundance of high-dimensional measurements in the form of gene expression and mass spectroscopy calls for models to elucidate the underlying biological system. For widely studied organisms like yeast, it is possible to incorporate prior knowledge from a variety of databases, an approach used in several recent studies. However if such information is not available for a particular organism these methods fall short. In this paper we propose a statistical method that is applicable to a dataset consisting of Liquid Chromatography-Mass Spectroscopy (LC-MS) and gene expression (DNA microarray) measurements from the same samples, to identify genes controlling the production of metabolites. Due to the high dimensionality of both LC-MS and DNA microarray data, dimension reduction and variable se

SUBMITTER: Brink-Jensen K 

PROVIDER: S-EPMC3783437 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature