Eigenvector metabolite analysis reveals dietary effects on the association among metabolite correlation patterns, gene expression, and phenotypes.

ABSTRACT:

Introduction

'Multi-omics' datasets obtained from an organism of interest reared under different environmental treatments are increasingly common. Identifying the links among metabolites and transcripts can help to elucidate our understanding of the impact of environment at different levels within the organism. However, many methods for characterizing physiological connections cannot address unidentified metabolites.

Objectives

Here, we use Eigenvector Metabolite Analysis (EvMA) to examine links between metabolomic, transcriptomic, and phenotypic variation data and to assess the impact of environmental factors on these associations. Unlike other methods, EvMA can be used to analyze datasets that include unidentified metabolites and unannotated transcripts.

Methods

To

SUBMITTER: Scott Chialvo CH 

PROVIDER: S-EPMC5568542 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature